SYSTEM AND METHOD FOR DESIGNATING VOLUMETRIC DIMENSIONS OF VASCULAR MATTER

FIELD OF THE DISCLOSURE Embodiments of the disclosure relate to designation of volumetric dimensions of vascular matter.

BACKGROUND

Images produced by CT equipment such as those captured from angiography are typically enhanced by the introduction of contrast material such as, for example, iodine based contrast materials into a patient's blood stream. Such contrast material may not be administered to all patients. Further, even with contrast material, images may not reveal or display accurate data about the volume of vascular matter such as blood, plaque and calcium deposits that may be present in a vessel.

SUMMARY OF THE DISCLOSURE

In some embodiments, a method may include defining an area of a component in a blood vessel in an image of the vessel captured by an ex vivo imager, where the blood vessel in the image is free or substantially free of contrast material. In some embodiments, the vessel component is selected from the group consisting of blood, plaque and calcified deposits.

In some embodiments, a method may include defining a three dimensional volume of the component in the vessel from a series of images of the vessel captured by the ex vivo imager. In some embodiments, a method may include selecting a first pixel in the image at a known position relative to a boundary of the vessel in the image; identifying the component from a measure of a brightness of the first pixel, relative to a pre-defined brightness range of the component; and designating a pixel proximate to the first pixel as depicting the component, where the designating is based on a brightness of the pixels relative to the first pixel.

In some embodiments, a method may include identifying a segment of a perimeter of a blood vessel in an image, where the image is captured by an ex vivo imager of a vessel free of contrast material; selecting a pixel of the image, where such pixel depicts an area within the vessel; comparing a brightness value of the pixel to a

pre-determined brightness value; and identifying a component of the blood vessel depicted by the pixel.

In some embodiments, a method may include evaluating a change in a geometric property of an isometric contour level of a cluster of pixels in the image of the vessel. In some embodiments, evaluating the change comprises selecting a contour level region from among several contour level regions, by comparing a geometric property of the contour level region to a geometric property of other contour level regions.

In some embodiments, a method may include identifying a component selected from the group of components of blood, calcium and plaque. In some embodiments, a method may include identifying a component as blood if a pixel is removed from the perimeter of thejyessel and ajbπghtness level of such pixel is between 20HU and 90HU.

In some embodiments, a method may include identifying a component as calcium if the pixel has a brightness level above 90HU. In some embodiments, a method may include identifying a component as plaque if such pixel is adjacent to a perimeter of the vessel and the pixel has a brightness level of below 20HU.

In some embodiments, a method may include estimating a position of the pixel relative to the perimeter of the vessel. In some embodiments, a method may include identifying a first pixel in an image of a blood vessel as depicting a component in the blood vessel; selecting a second pixel proximate to the first pixel; selecting a first set of pixels proximate to the second pixel; identifying a pixel in the first set of pixels as depicting the component; calculating a brightness value of the pixel in the first set of pixels that depicts the component; calculating a difference between the brightness value of the pixel in the first set of pixels that depicts the component and a brightness value of another pixel in the first set of pixels; and selecting a second set of pixels from among the set, where the second set has pixels with a lower of the difference than other pixels in the first set.

In some embodiments, a method may include executing a non-parametric discriminator on the differences of brightness levels for a plurality of pixels in the first set of pixels from the brightness value of the pixel in the first set that depicts the component.

In some embodiments, a method may include executing the non-parametric discriminator as a Fischer discriminator.

In some embodiments, a method may include identifying the first pixel as depicting a component selected from the group consisting of blood, plaque and calcium.

In some embodiments, a method may define a volumetric space of a blood vessel in an image by convolving a first value associated with a first pixel in the image of the blood vessel from a second value of a plurality of pixels in a defined area around the first pixel; and comparing the first value of the first pixel to the first value of a second pixel to determine that the first pixel and the second pixel are included in a volumetric space of a component in the blood vessel. In some embodiments, a method may include defining a set of pixels in an area around a blood vessel; assigning to a second pixel, where the second pixel is included in the set of pixels around the first pixel, a first value corresponding to a component of the blood vessel represented by the second pixel and a second value corresponding to a distance of the second pixel from the first pixel; assigning to the first pixel a third value, that is a function of the first value and the second value; designating the first pixel as corresponding to a component of the blood vessel on the basis of a magnitude of the third value.

In some embodiments, a set of pixels includes a Euclidian distance matrix around the first pixel.

In some embodiments, the Euclidian distance matrix comprises a 3 x 3 x 3 matrix around the first pixel.

In some embodiments, a first value is assigned a 2 if the second pixel corresponds to blood, -1 if the second pixel corresponds to plaque and -0.5 if the second pixel corresponds to a calcified deposit.

In some embodiments, the second value is denominated in units of a Euclidian distance matrix as the distance between the second pixel and the first pixel.

In some embodiments, a method may include assigning to the pixel the value calculated as the product of the first value and the second value. In some embodiments, a method may include convolving the third value assigned to the first pixel from the function for a plurality of pixels in the set of pixels.

In some embodiments, a method may include designating the first pixel as blood in the blood vessel if the third value is greater than 0.

In some embodiments, a method for defining a boundary of a blood vessel in an image captured by an ex vivo imager may include calculating a point approximating a center of the vessel; mapping a plurality of perpendicular lines outwards from a point on the center line, the perpendicular lines intersecting a boundary of the vessel; and locating an area of low homogeneity along a perpendicular line of the perpendicular lines.

In some embodiments, a method may include mapping coordinates of a plurality of areas of low homogeneity into a circumferential boundary of the vessel.

In some embodiments, a method may include determining a volume of an inner portion of the vessel that contains blood. In some_embodiments, ajnetho4rnayjnckιdj^alculating^^lop^ofthe line.

In addition to the exemplary aspects and embodiments described above, further aspects and embodiments will become apparent by reference to the figures and by study of the following detailed description.

BRIEF DESCRIPTION OF THE FIGURES

Exemplary embodiments are illustrated in referenced figures. Dimensions of components and features shown in the figures are generally chosen for convenience and clarity of presentation and are not necessarily shown to scale. It is intended that the embodiments and figures disclosed herein are to be considered illustrative rather than restrictive. The figures are listed below.

Fig. 1 is a schematic diagram of an image processing device and system; Fig. 2 is a depiction of a series of images;

Fig. 3 is a schematic depiction of isolable contour regions defining areas of image intensities of pixels;

Fig. 4 is a flow diagram of a method; Fig. 5 is an isometric view of found pixels; Fig. 6 is an isometric view of found blood pixels; Fig. 7 shows a volume of component matter in a blood vessel; Fig. 8 is a simplified processed image of a blood vessel; and

Fig. 9 is a simplified depiction of a homogeneity of pixels in an image of a blood vessel.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following description, various embodiments of the invention will be described. For purposes of explanation, specific examples are set forth in order to provide a thorough understanding of at least one embodiment of the invention. However, it will also be apparent to one skilled in the art that other embodiments of the invention are not limited to the examples described herein. Furthermore, well- known features may be omitted or simplified in order not to obscure embodiments of the invention described herein.

Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification, discussions utilizing terms such as "selecting," ^processing," "computing," "calculating," "determining," or the like, may refer to the actions and/or processes of a computer, computer processor or computing system, or similar electronic computing device, that may manipulate and/or transform data represented as physical, such as electronic, quantities within the computing system's registers and/or memories into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices. In some embodiments processing, computing, calculating, determining, and other data manipulations may be performed by one or more processors that may in some embodiments be linked. The processes and functions presented herein are not inherently related to any particular computer, image, network or other apparatus. Embodiments of the invention described herein are not described with reference to any particular programming language, machine code, etc. It will be appreciated that a variety of programming languages, network systems, protocols or hardware configurations may be used to implement the teachings of the embodiments of the invention as described herein.

In some embodiments, the term "free of contrast material" or "not highlighted by contrast material" may, in addition to the regular understanding of such term, mean having contrast material in quantities that are insufficient to provide a clear or visibly distinct definition of the boundaries of the lumen of a vessel wherein such contrast material may be found. In some embodiments, the term "free of contrast material" may mean that a contrast material was not administered. According to some embodiments, the term "free of contrast material" may also mean no contrast material, lower amounts than normal of contrast material, lower concentration than normal of contrast material, a mixture of varying amounts of various contrast materials, trace amounts of contrast

material, and/or various kinds of contrast materials, that may be different than the regularly used contrast material.

According to some embodiments, contrast material may be used for highlighting a subject body or at least part of a body, such as, for example a limb; organ(s), such as internal organs, paired organs, symmetrical organs, individual organs; tissue(s), such as a soft tissue, hard tissue; cells; body vessels, such as a blood vessel, a tree of blood vessels, alimentary canal, urinary tract, reproductive tract, tubular vessels, receptacles and the like, or any combination thereof.

The contrast material may include any suitable contrast material or a combination of contrast material with other substances and agents such as, for exampje,_additional contrast material, carriers, buffers, saline, dilutents, solvents, body fluids and the like. Suitable contrast materials may include such materials as, but not limited to: Iodine, isotopic forms of Iodine, such as radioactive Iodine, Gadolinium, Gadolinium Chelates, micro-bubbles agent or any other suitable material that may be used as contrast material.

The contrast material may be administered to the subject body, or part of a body, as detailed hereinabove, by various ways, such as, for example, by inhalation, by ingestion, by injection, by rectal insertion or any other appropriate route of administration and any combination thereof. Contrast material may be secludedly administered. Contrast material may be administered, for example, in the form of a bolus wherein the contrast material may be mixed, prior to administration with a fluid. For example, the bolus may include a contrast material and saline. For example, the bolus may include a contrast material and a blood sample. In addition, after administration of the bolus, a saline push may be administrated. Saline push may include an additional administration of saline, (for example, in a volume of 20-50 ml) that may be administered in a short time (such as between 1 to 60 seconds) after administration of the bolus containing the tracing material.

Administration of the contrast material to the subject may allow a spatially and/or temporally tracing of the contrast material in the subject, which may be used in a method according to some embodiments. Tracing the contrast material may be performed at various spatial (locations/regions) and temporal (time points) distributions. For example, tracing contrast material may be performed in a region that is located at a spatial and/or temporal region to which the bolus has not yet reached. For example, tracing contrast material may be performed in a region that is located at a

spatial and/or temporal region that is correlated with the location of the bolus. For example, tracing contrast material may be performed in a region that is located at a spatial and/or temporal region to which the bolus has already reached and passed the location of the tracing region. Tracing the contrast material may include tracing a high amount/concentration of contrast material. A high amount/concentration of contrast material may include, for example, about 75%- 100% of the amount of contrast material administered. Tracing the contrast material may include tracing an average amount/concentration of contrast material. An average amount/concentration of contrast material may include, for example, about 50%-75% of the amount of contrast material administered. TracingJfae contrasLmateriaLmay include tracing a low amount/concentration of contrast material. A low amount/concentration of contrast material may include, for example, about 25%-50% of the amount of contrast material administered. Tracing the contrast material may include tracing a trace amount/concentration of contrast material. A trace amount/concentration of contrast material may include, for example, about 0.000001% - 25% of the amount of contrast material administered. More preferably, trace amounts may include about .000001% -2.5% of the amount of contrast material administered. Even more preferably, trace amounts may include about .000001% - 1% of the amount of contrast material administered. Even more preferably, trace amounts may include about .000001% - 0.05% of the amount of contrast material administered. Even more preferably, trace amounts may include about .000001% - 0.01% of the amount of contrast material administered. Even more preferably, trace amounts may include about .000001% - 0.005% of the amount of contrast material administered. Even more preferably, trace amounts may include about .000001% - 0.0005% of the amount of contrast material administered. Tracing the contrast material may further include tracing the absence of contrast material (0%).

As mentioned hereinabove, contrast material administered may sometimes result in an adverse effect on subjects to which the contrast material was administered.

Some subjects may experience severe and potentially life threatening reactions, such as, for example allergic reactions to the contrast material. In addition, the contrast material may also induce organ damage, such as, for example, damage to the kidneys of a user, in particular with users that have a preexisting renal insufficiency, preexisting diabetes or reduced intravascular volume. In addition, the contrast material

is economically expensive. There is thus a need to lower the amount/concentration of the contrast material used.

According to some embodiments, the contrast material administered to a subject, such as, for example, in the form of a bolus, may include a lower percentage of contrast material than is routinely used. For example, according to some embodiments, the bolus injected into a subject may include 0.1%-50% of contrast material. The contrast material administered to a subject, such as, for example, in the form of a bolus, may include 0. l%-40% of contrast material. Preferably, the contrast material administered to a subject, such as, for example, in the form of a bolus, may include 0.1%-25% of contrast material. Even more preferably, the contrast material administered to a subject, such as, for _ejcarnpie ₃ jn tfø form jxf a bolus, may include

0.1%- 10% of contrast material. Even more preferably, contrast material administered to a subject, such as, for example, in the form of a bolus, may include 0.1%-5% of contrast material. Even more preferably, contrast material administered to a subject, such as, for example, in the form of a bolus, may include 0.1%-2% of contrast material.

According to some embodiments, the contrast material administered to a subject, such as, for example, in the form of a bolus, may include a lower volume of contrast material. For example, a volume of contrast material that is routinely used is at a range of about 80-150 ml. As a non-limiting example, an Iodine containing contrast material, such as UltaVist (at a concentration of 370 mg/dl) may be used. As another, non-limiting example, Gadolinium containing contrast material may be used. According to some embodiments, a volume of contrast material that may be used in a method according to some embodiments may include about 0.1-60 ml. Preferably, a volume of contrast material at the above mentioned concentration that may be used in a method according to some embodiments may include about 0.1-40 ml. More preferably, a volume of contrast material at the above mentioned concentration that may be used in a method according to some embodiments may include about 0.1-20 ml. Even more preferably, a volume of contrast material at the above mentioned concentration that may be used in a method according to some embodiments may include about 0.1-10 ml. Even more preferably, a volume of contrast material at the above mentioned concentration that may be used in a method according to some embodiments may include about 0.1-5 ml. Even more preferably, a volume of contrast

material at the above mentioned concentration that may be used in a method according to some embodiments may include about 0.1-2 ml.

According to some embodiments, the contrast material administered to a subject, such as,for example, in the form of a bolus, may include lower amounts of contrast material. For example, an amount of contrast material, such as UltaVist that is routinely used, is at a range of about 290-600 mg. According to some embodiments, an amount of contrast material that may be used in a method according to some embodiments may include about 0.1-500 mg. According to some embodiments, an amount of contrast material that may be used in a method according to some embodiments may include about 0.1-400 mg. Preferably, an amount of contrast material that may be used in a method according to some embodiments may include about 0.1-300 mg. More preferably, an amount of contrast material that may be used in a method according to some embodiments may include about 0.1-200 mg. Even more preferably, an amount of contrast material that may be used in a method according to some embodiments may include about 0.1-100 mg. Even more preferably, an amount of contrast material that may be used in a method according to some embodiments may include about 0.1-50 mg. Even more preferably, an amount of contrast material that may be used in a method according to some embodiments may include about 0.1-20 mg. Even more preferably, an amount of contrast material that may be used in a method according to some embodiments may include about 0.1-10 mg. Even more preferably, an amount of contrast material that may be used in a method according to some embodiments may include about 0.1-1 mg.

Reducing the flow rate of contrast material after administration may be used in a system and method according to some embodiments. Lowering the flow rate of contrast material after administration may be as a result of, for example, reduced heart output, reduced blood flow, reduced administration rate, and any combination thereof.

According to some embodiments, the contrast material administered to a subject, such as, for example, in the form of a bolus, may include a lower administration rate of contrast material. For example, an administration rate, for example by injection, of contrast material, such as UltaVist that is routinely used, is at a range of about 2-5 ml/second. According to some embodiments, an administration rate of contrast material that may be used in a method according to some embodiments may include an administration rate of about 0.05ml-2ml/sec. Preferably, an administration rate of contrast material that may be used in a method according to

some embodiments may include an administration rate of about 0.05ml-1.5 ml/sec. More preferably, an administration rate of contrast material that may be used in a method according to some embodiments may include an administration rate of about 0.05ml- 1 ml/sec. Even more preferably, an administration rate of contrast material 5 that may be used in a method according to some embodiments may include an administration rate of about 0.05ml-0.75ml/sec. Even more preferably, an administration rate of contrast material that may be used in a method according to some embodiments may include an administration rate of about 0.05ml-0.5 ml/sec. Even more preferably, an administration rate of contrast material that may be used in a

10 method according to some embodiments may include an administration rate of about

— 0.05ml-0.25 ml/sec. Even more preferably, an administration rate of contrast material that may be used in a method according to some embodiments may include an administration rate of about 0.05ml-0.1 ml/sec.

According to some embodiments, the contrast material administered to a

15 subject may include a Gadolinium containing contrast material. Gadolinium may be regularly/routinely used in applications such as, for example, MRI, at a dosage of 0.1- 0.3 mmole/kg. Thus, an average weight adult subject may be administered with (dependant on the subject's weight) about 20-40 ml of a contrast material containing Gadolinium. Usually, the Gadolinium containing contrast material is not mixed or

20 diluted with saline or other material, and it may be administered by any administration route. When administered by, for example, injection, an injection rate may be 1-3 ml/sec and may be followed by a saline push of, for example, 20 ml. Gadolinium containing contrast material is not routinely used for applications such as a CT. When rarely used for such applications, the dosage used may be up to 4 times higher than

25 that used for an application such as an MRI. However, high amounts of Gadolinium containing contrast material may impose health hazard to subjects administered with the material by causing severe side effects. According to some embodiments, contrast material containing Gadolinium may be used in applications such as a CT, in a method and system in accordance with some embodiments. The Gadolinium material used

30 according to some embodiments, may include Gadolinium containing contrast material at a dosage of about 0.001-0.25 mmole/kg. Preferably, Gadolinium containing contrast material may be used at a dosage of about 0.001-0.20 mmole/kg. More preferably, Gadolinium containing contrast material may be used at a dosage of about 0.001-0.15 mmole/kg. More preferably, Gadolinium containing contrast material may

be used at a dosage of about 0.001-0.10 mmole/kg. Even more preferably, Gadolinium containing contrast material may be used at a dosage of about 0.001-0.05 mmole/kg. Even more preferably, Gadolinium containing contrast material may be used at a dosage of about 0.001-0.01 mmole/kg. The Gadolinium material used according to some embodiments may include administration of Gadolinium containing contrast material at an administration rate of about 0.01-2.5 ml/sec. Preferably, the Gadolinium material used according to some embodiments may include administration of Gadolinium containing contrast material at an administration rate of about 0.01-2 ml/sec. More preferably, the Gadolinium material used according to some embodiments, may include administration of Gadolinium containing contrast material at an administration rate of about 0.01-1.5 ml/sec. Preferably, the Gadolinium material used according to some embodiments may include administration of Gadolinium containing contrast material at an administration rate of about 0.01-1 ml/sec. Even more preferably, the Gadolinium material used according to some embodiments may include administration of Gadolinium containing contrast material at an administration rate of about 0.01-0.5 ml/sec. Preferably, the Gadolinium material used according to some embodiments may include administration of Gadolinium containing contrast material at an administration rate of about 0.01-0.2 ml/sec. Even more preferably, the Gadolinium material used according to some embodiments may include administration of Gadolinium containing contrast material at an administration rate of about 0.01-1 ml/sec.

Lowering the percentage and/or volume and/or amount and/or administration rate of contrast material administered to a subject may lower the spatial and temporal detection levels of the contrast material and therefore, to overcome this potential problem, enhancement and a method for enhancement of detection and tracing and/or accuracy of detection and tracing is provided, according to some embodiments.

Reference is made to Fig 1, a schematic diagram of an image processing device and system, in accordance with an embodiment of the invention. An image processing device in accordance with an embodiment of the invention may be or include a processor 100 such as, for example, a central processing unit. The image processing device may include or be connected to a memory unit 102 such as a hard drive, random access memory, read only memory or other mass data storage unit. In some embodiments, processor 100 may include or be connected to a magnetic disk drive 104 such as may be used with a floppy disc, disc on key or other storage device. The image

processor may include or be connected to one or more displays 106 and to an input device such as, for example, a keyboard 108 A, a mouse, or other pointing device 108B or input device by which, for example, a user may indicate to processor 100 a selection or area that may be shown on a display. In some embodiments, processor 100 may be adapted to execute a computer program or other instructions so as to perform a method in accordance with embodiments of the invention.

Processor 100 may be connected to an external or ex vivo diagnostic imager 110, such as, for example, a computerized tomography (CT) device, magnetic resonance (MR) device, ultrasound scanner, CT Angiography, magnetic resonance angiograph, positron emission tomography or other imagers 110. In some embodiments, imager 110 may capture one or more images of a body 112 or body part such as, for example, a blood vessel 114, a tree of blood vessels, alimentary canal, urinary tract, reproductive tract, or other tubular vessels or receptacles, In some embodiments, imager 110 or processor 100 may combine one or more images or series of images to create a 3D image or volumetric data set of an area of interest of a body or body part such as, for example, a blood vessel 114. In some embodiments, a body part may include a urinary tract, a reproductive tract, a bile duct, nerve or other tubular part or organ that may, for example, normally be filled or contain a body fluid. In some embodiments, imager 110 and/or processor 100 may be connected to a display 106 such as a monitor, screen, or projector upon which one or more images may be displayed or viewed by a user.

Reference is made to Fig 2, a depiction of a series of images in accordance with an embodiment of the invention, hi some embodiments, a series of images 200 may be arranged, for example, in an order that may, when such images 200 are stacked, joined or fused by, for example, a processor, create a three dimensional view of a body part such as a blood vessel 114, or provide volumetric data on a body part or structure. In some embodiments, images 200 in a series of images may be numbered sequentially or otherwise ordered in a defined sequence. In some embodiments, images 200 may include an arrangement, matrix or collection of pixels 202, voxels or other atomistic units that may, when combined, create an image. In some embodiments, pixels 202 may exhibit, characterize, display or manifest an image intensity of the body part appearing in the area of the image 200 corresponding to pixel 202. In some embodiments, an image intensity of a pixel 202 may be measured in Hounsfleld units (HU) or in other units.

In some embodiments, certain clusters of pixels may be mapped into regions of isolable contour levels, where a mapped region shows an area of a cluster of pixels having a given range of image intensities. In some embodiments, a selection of one among a plurality of possible isolable contour regions may define or enhance the accuracy of one or more boundaries of a target vessel. A selection of an isolable contour level that may define a boundary of a vessel may be made by, for example, comparing geometric characteristics of a view of, for example, a target vessel or other area as it is presented in, for example, two or more isolable contour regions.

In accordance with an embodiment of the disclosure, vessel segmentation may be performed using a region growing method, such as, for example, isolable contour or region growing. Reference is made to Fig 3, a schematic depiction of isolable contour regions defining areas of image intensities of pixels in accordance with an embodiment of the invention. In some embodiments, an isolable region 301 with a lowest level may define an area of pixels having an intensity level of at least, for example, -1000HU. Another isolable contour region 302 may define an area that includes pixels having intensities of at least -100HU, another intensity level may define an area with pixels having intensity levels of at least OHU, another isolable region may define an area having pixels with, for example, 60HU, another isolable region 303 may define an area having pixels with, for example, 200HU, and another isolable region 304 may define an area having pixels with, for example, 400HU.

In some embodiments, an evaluation of the shape or other geometric properties of contour level regions may be used to determine a contour level that most closely defines a boundary of a target vessel. One such evaluation may include a comparison of shapes or other geometric properties of the areas of pixels encompassed by the various contour regions depicted on an overlaid map of the clustered pixels. Such a comparison may include calculating a minimum derivative of δAD, where δAD - δArea * δDistance, where δArea is the change in the total area between two isolable contour regions in a clustered area of an image, and δDistance is the distance along the x and y axis between a center of mass of such two isolable contour regions. In some embodiments, contour region i+1 may be selected, where i is the contour region in respect of which δAD crosses the x axis to denote a zero change in δAD between the relevant contour regions. For example, and returning to Fig. 3, if in a comparison of contour region 301 and contour region 302, δAD is zero, contour region 302 may be selected as defining a boundary of a target vessel, If there is more than one phase of

δAD crossing the x axis, the first point in the second phase may be selected. Other methods of selecting a contour region that defines a boundary of a target vessel may be used.

In some embodiments, defining a boundary of a vessel may include identifying an isolable contour unit that includes a high or highest intensity level of pixels in the image. A shape of such isolable contour unit may be evaluated and described via, for example, a shape descriptor function. It may initially be assumed that within a range of variation, a shape of a highest isolable contour unit may predict a shape of the vessel wherein such isolable contour unit is found. A level of image intensity levels included in an isolable contour unit may be lowered to expand the isolable contour unit, and a comparison between the prior isolable contour unit and the current isolable contour unit may be made. This process may be stopped, for example, at the lowest level isolable contour unit that does not significantly alter the shape of the subject vessel.

The intensity levels of the pixels in the subject vessel defined by the isolable contour unit may be evaluated. Of such pixels, the pixels having the highest intensity levels, where such intensity levels are also above 90HU, may be designated as found calcified deposits. Pixels in an area away from the center of the subject vessel that are also below 20HU may be classified as found plaque. Pixels in an area approximating a center of the subject vessel that have intensities of between 20HU and 90 HU may be defined as found blood. Other definitions may be used and other intensities may be approximated to designate components found in the vessel.

In some embodiments, one or more pixels in an image that is designated as any of found blood, found calcified deposits or found plaque may be assigned a marker, and a location coordinate of such pixels may be recorded and stored. Reference is made to Fig 4, a flow diagram of a method, in accordance with an embodiment of the invention. In some embodiments, and as shown in block 400, a boundary or perimeter or segment of a boundary of a perimeter of a blood vessel may be identified. In some embodiments, such boundary may be identified by varying isolable contour levels of intensities of pixels in an area of a subject vessel. In block 402, an image intensity or brightness of one or more pixels within a boundary of a subject vessel may be evaluated on a brightness scale, and a position of such pixel relative to a center or boundary of the vessel may be identified.

In block 404, the intensity level of one or more pixels may be compared to predefined levels to designate the pixel as representing a component or material that

may be present in a blood vessel. Such components may include, for example, blood, calcified deposits, plaque or others.

Reference is made to Fig. 5, a conceptual depiction of found pixels within a designated three dimensional array of adjacent pixels, in accordance with an embodiment of the invention. In some embodiments, a pixel of found blood as was described in this paper, and whose coordinates are known, may be designated. A multidimensional array, such as a 3D array of pixels that are adjacent to or surrounding the designated pixel, and whose coordinates may be derived therefrom may be defined. In some embodiments, the designated found blood pixel may be located at the center of the array. Other positions are possible, and other size arrays are possible.

In some embodiments, the brightness, image intensity or HU level of the designated pixel and other found blood pixels that may be located within the, for example, 27 pixels in the array may be recalled from a stored memory, and, for example, an average intensity of such found blood pixels may be computed, and saved as dθ. dO may be subtracted from the pixel intensity of the individual pixels in the array, or from the non-found blood pixels in the array, and the absolute values of the result may be recorded for each such pixel as di. The resulting di values may be sorted in, for example, ascending order. A discriminator, such as, for example, a Fischer discriminator or other non-parametric discriminator that may separate a set of numbers without designating a priori parameters may be applied to the sorted list. The discriminator may sort the di number sets into two or more groups. The group of pixels with the lower of the di values may be designated as assumed blood, and the pixels so designated may be marked.

The process of selecting a designated found blood pixel, assembling an array around such found blood pixel, and deriving assumed blood pixels in the assembled array may be repeated for some or all of the found blood pixels in an image or series of images. Similarly, the process may be repeated for found calcified deposit pixels such that pixels in low di group as may surround found calcified deposits may be deemed assumed calcified deposits, and pixels in the low di group surrounding plaque may be deemed assumed plaque.

In some embodiments, the proximity of a pixel to a known blood pixel, and the similarity in brightness levels of such proximate pixel to the known blood pixel may serve as sufficient basis for the assumption that such proximate pixel represents blood in the image. In some embodiments, pixels that are deemed assumed blood may be

displayed in an image of a vessel as blood, while assumed calcified deposits and assumed plaque may be displayed as plaque or calcified products respectively. In this paper, the union of the set of found blood pixels and assumed blood pixels may be called assumed blood pixels. Likewise, the union of the set of found calcified deposit pixels and assumed calcified deposit pixels may be called assumed calcified deposit pixels.

In some embodiments, a set of assumed blood pixels within a blood vessel may define an area of blood in a two dimensional image of a blood vessel. When such set of assumed blood pixels is assembled in a series of images of such vessel, such set may define a volume of blood in a vessel. Areas or volumes of plaque and calcified deposits may also be identified in one or a series of images of vessels.

Reference is made to Fig. 6, a conceptual illustration of found blood pixels and assumed blood pixels along with undesignated pixels. In some embodiments, a designation and display of known and assumed pixels may yield noise or undefined pixels that may distort or impede smooth viewing of an area in an image. In some embodiments, such undesignated pixels that may be interspersed among known and assumed pixels may be defined by a value based array process.

In the value based array process, an array such as, for example, a multidimensional array such as, for example, a 3x3x3 array may be designated around a subject pixel, where the subject pixel may be situated at the center or kernel of such array. One, some or all of the pixels in the array may be assigned a value inversely based on the distance or proximity, such as, for example, a Euclidian distance, of such pixel from the kernel pixel, For example, a pixel that is in contact with the kernel or that is a single jump from the kernel may be designated with a +3, while a pixel that is on a corner or an outside plane of the array may be valued with a +1. A second value, such as a vessel component value, may be assigned to one, some or all of the pixels in the array based on the prior designation of such pixels from a prior process as any of assumed blood, assumed calcified deposits or assumed plaque. For example, assumed blood pixels that may be present in the array may be assigned a component value of +1, assumed plaque pixels in the array may be assigned a component value of -1, and assumed calcified deposit pixels may be assigned a value -0.5.

In some embodiments, the product of the component values times the distance values may be convolved upon a kernel pixel. In some embodiments, if a value assigned to a kernel pixel is greater than 0, such kernel pixel may be designated as

blood pixel for purposes of display and the calculation of an area or volume of blood. In some embodiments, this process may reduce the number of unknown or undesignated pixels in an image or volume of matter in a blood vessel.

An alternative method is to perform blood vessel segmentation using a region growing from an initial seed point. An example for a region growing method is Confidence Connected. In this algorithm, a mean and standard deviation of HU values is calculated on pixels around the seed point. Using a predefined constant, ranges of HU average values and HU standard deviation are created. From that point, all connected pixels that are within those ranges are added to the segmented volume. The next step is to recalculate the HU average and standard deviation and create new ranges of HU average and HU standard deviation based on the result volume. The process repeats until no more pixels are added or a predefined number of cycles have passed.

Reference is made to figure 7. The result segmented volume can then be used to calculate the centerline of such volume. A centerline extraction can be based on various methods such as Flux Driven Centerline Extraction, further discussed in Sylvain Bouix, Kaleem Siddiqi, Allen Tannenbaum, Flux driven automatic centerline extraction, Medical Image Analysis 9 (2005) 209-221, incorporated herein by reference. Another advantageous method for extracting the centerline is as follows: A common approach to extract a centerline from a segmented object (for example, vessel) is to use its distance map. The centerline is contained in the resulted ridge surface. The centerline may be extracted as a tree of minimal weight paths from source points to target points (for example, a source point could lie on the aorta and the target points could lie on the distal tip of each vessel in the abdominal tree). Each point inside the object was assigned with a weight function.

Another common approach is to compute a weighted distance map using the fast marching approach and then to trace back the centerline from maximal points on each level set.

The advantageous concept is a combination of the two common approaches. The fast marching technique is used here on the distance map (rather than on image gray levels), in order to design a very good weight function.

Assuming a coarse (or initial) segmentation of the object (as described from the region growing step) as well as source/target points are known, the algorithm may work as follows:

1. Applying binary threshold on the initial (coarse) vessel segmentation. This step results in l's inside the vessel and O's elsewhere.

2. Calculating distance map in respect to the outer layer (edges) of the vessel. Each point inside the vessel is assigned with its Euclidian distance from the outer layer.

3. Determining a seed surface by extracting the outer layer of the vessel.

4. Determining a volumetric speed map by: a. Computing the gradient of the distance map. b. Applying a Sigmoid on the gradient. The speed map is designed in such a way that the front's propagation speed is very low as it approaches the seed surface (high gradients) and rather fast towards the ridge of the distance transform (low gradients). This will make the contour propagate until it reaches the centerline.

5. Applying Fast Marching on the volumetric speed map. The resulted level sets act as a basis to the weight function.

6. Applying a mathematical transformation such that each point in the vessel is assigned with a weight that is proportionally inverted to its level set value.

7. Constructing a directed graph from the segmented vessel. The graph is designed in such a way that each voxel is a vertex in the graph, and each pair of neighboring voxels (26-connected) are connected by two anti parallel edges. Each edge is assigned with a positive weight that equals to the weight associated with its target voxel.

8. Using Dijkstra algorithm in order to find one source multiple targets minimal paths (in other words, centerline). The Dijkstra algorithm is well known in the arts, and is further explained in "Dijkstra's algorithm." Wikipedia, The

Free Encyclopedia. 4 JuI 2008, 14:40 UTC. Wikimedia Foundation, Inc. 6 JuI. 2008 <http://en.wikipedia.org/w/index.php?title=Dijkstra%27s _algorithm&oldid=223537930>.

Reference is made to Fig. 8, a simplified cross-sectional, processed image of a blood vessel with a designated center line and virtual radii extending therefrom intersecting a boundary of the vessel. Fig. 8 shows a cross section designated "A" in Fig. 7. In some embodiments, identification of a center line of a subject vessel may permit calculation of a slope of the vessel. With the center line and slope known, a representation of one or more slices or cross-sections of the vessel, such cross-sections

being on a plane having a light angle to the slope of the center line, may be performed. Along such plane there may be designated radii originating, for example, from the center line and at a light angle thereto, and continuing outward along the plane of the slice of the vessel. Such radii may be assumed to intersect with the boundary of the vessel at some distance from the center line.

After calculating the centerline, a finer (or final) segmentation may be performed using the image data and the calculated centerline. One alternative is shown in Fig 9, a simplified depiction of a homogeneity of pixels in an image of a blood vessel, in accordance with an embodiment of the invention. In some embodiments, the radii originating from and, for example, perpendicular to the designated center line of a vessel may intersect numerous pixels. Such intersected pixels may depict or represent various components that may be present in a vessel. For example, from a center line of a vessel it may be assumed that the radii will intersect pixels that may represent blood. A segment of a radius passing through an area or volume near, for example, a center line of a vessel that may contain blood may intersect pixels that are homogeneously blood pixels. In some embodiments, a homogeneity measure may be calculated by locating a mass center of blood pixels intersected by the radii and a mass center of not- blood pixels intersected by such radius. If the center of the mass of blood pixels and not-blood pixels at or around a given segment of the radius are the same or are similarly located, then the pixels in or around such segment may be said to be homogenous, or exhibiting a high level of homogeneity. If the center of mass of blood pixels and not-blood pixels in the area of the segment of the radius are not the same or are not similarly located, then such area may be non-homogenous or exhibiting a low level of homogeneity. In some embodiments, the radii around one or more points or areas of the center line may be plotted onto a single axis to produce a two-dimensional depiction of the radii, where the center point or center line is represented by the left column, and each column along the x axis represents one or more pixels or groups of pixels along the radius. In some embodiments, a homogeneity of a set of pixels around a radius may be calculated by clustering the pixels in the set into two or a limited number of clusters. An average brightness for each of the respective clusters may be calculated, and a variance of the actual brightness of pixels in the respective clusters from the average brightness of such cluster may be calculated.

In some embodiments, a reduction in homogeneity of pixels intersected by a radius may indicate that the radius has passed from an area of blood pixels to an area of, for example, plaque or calcified deposit pixels. A large reduction in homogeneity along a radius may indicate that the radius has passed into a wall of a blood vessel At some point along a radius, there may be intersected pixels that represent one or more of plaque, calcified deposits and finally an inside wall and an outside wall of a vessel.

Another alternative is to use the centerline pixels as a starting volume for a level set-based segmentation, for example Active Contours. In level set segmentation, a curve evolution function is initiated from a zero level using a differential equation. The center line pixels can be used as the zero level. A specific example of such function is described in M. Leventon, E. Grimson, and O. Faugeras. Statistical shape influence in geodesic active contours. Proc. IEEE Conf. Comp. Vision and Patt. Recog., 2000, http.7/ www.leventon.com/mit/Research/0006-CVPR/cvpr00.pdf, incorporated herein by reference. Leventon et al. describe active contours segmentation using statistical shape guidance.

The evolution of the level set function can be based on several image features such as the average HU values, gradients of HU values, homogeneity of HU values or other textural features of the images. The active contours evolution can then grow from the centerline until a point where there is a shift in homogeneity, or growing while maintaining a minimal change in the homogeneity values.

A detailed description of homogeneity, texture features and analysis

In many CT images, different tissues or organs may not be differentiated based on their characteristic HU values, which may overlap significantly, but rather based on their textural qualities and features. A texture map may be calculated from the original

CT image, and then a segmentation algorithm may be deployed in order to classify this texture map into different tissue types.

In accordance with an embodiment of the disclosure, a step may include the creation of a 3D texture image date. This may be accomplished, for example, by using various methods for tissue classification and segmentation based on texture, such as, for example: J-value texture analysis, Gabor filter, Grey Level Co-occurrence Matrix

(GL-CM), Markov Random Fields (MRF), or any combination thereof.

J- value texture (JT) analysis is performed to extract textural information from the volume data. In a first step, the number of gray values in the volume data is reduced through quantization. An example of a method of quantization may be that used by the JPEG (Joint Photographic Experts Group). This results in a map in which each pixel is represented by a class label. In a second step, J-value, which is the ratio of in-class variance to between class variance, is calculated for each voxel. J-values correspond to measurements of local homogeneities at different scales, which can indicate potential boundary locations. For example, for an image consisting of several homogeneous regions, the gray value classes are more separated from each other and the J-value is large. Additional information on JT segmentation algorithm may be found in: Y. Deng and B. Manjunath, "Unsupervised segmentation of color-texture regions in images and video", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 8, pp. 800—810, 2001, incorporated herein by reference, in its entirety. According to some embodiments, textural information may be extracted from texture measures based on the grey level co-occurrence matrix (GL-CM) as described in: Haralick, R.M., et.al "Textural Features for Image Classification", IEEE Transactions on Systems, Man and Cybernetics. SMC-3(6):610-620, 1973, incorporated herein by reference, in its entirety; and Haralick, R.M. "Statistical and Structural Approaches to Texture", Proceedings of the IEEE, 67:786-804, 1979, incorporated herein by reference, in its entirety.

In some embodiments of the disclosure, textural information may be extracted from the volume data by applying a bank of Gabor filters to the image. The resulting texture vectors may then be clustered. Additional information may be found in: P. Kruizinga, N. Petkov and S.E. Grigorescu, "Comparison of texture features based on Gabor filters", Proceedings of the 10th International Conference on Image Analysis and Processing, Venice, Italy, September 27-29, 1999, pp.142- 147, incorporated herein by reference, in its entirety.

In some embodiments of the disclosure, textural information may be extracted from the volume data by the use of Markov Random Fields (MRF). MRF comprises the use of probabilistic models that use a correlation between pixels in a neighborhood to decide an object region. The MRF may optionally use maximum a posteriori (MAP) estimates for modeling the MRF. The object traverses a data set and uses a model generated by a distance classifier, such as, for example, Mahalanobis distance

classifier, to determine a distance between each pixel in the data set to a set of known classes. The distances may then be updated by evaluating the influence of neighboring pixels, and each pixel may be classified to the class which has the minimum distance to that pixel. Energy function minimization may be done, optionally using an iterated conditional modes (ICM) algorithm. Additional information may be found in: Chellappa, R., et.al., "Classification of textures using Gaussian Markov random fields", IEEE Transactions on Signal Processing, Volume 33, Issue 4, Aug 1985 Page(s): 959 - 96, incorporated herein by reference, in its entirety.

While a number of exemplary aspects and embodiments have been discussed above, those of skill in the art will recognize certain modifications, permutations, additions and sub-combinations thereof. It is therefore intended that the following appended claim and claims hereafter introduced be interpreted to include all such modifications, permutations, additions and sub-combinations as are within their true spirit and scope. In the description and claims of the application, each of the words "comprise"

"include" and "have", and forms thereof, are not necessarily limited to members in a list with which the words may be associated.