SUBTRACTION ALGORITHM FOR DETECTION OF TUMORS

CROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] The present application claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 62/545,410, filed August 14, 2017, entitled "SUBTRACTION ALGORITHM FOR DETECTION OF TUMORS", and U.S. Patent Application No. 16/102,449, filed August 13, 2018, entitled "SUBTRACTION ALGORITHM FOR DETECTION OF TUMORS", the entire content of which is incorporated herein by reference.

[0002] The present application is related to U.S. Patent Application Ser. No. 15/489,652, filed on April 17, 2017 entitled "SYSTEM AND METHOD FOR COMBINING 3D FMAGES IN COLOR", the entire content of which is incorporated herein by reference.

FIELD

[0003] One or more aspects of embodiments according to the present invention relate to systems and methods for detecting tumors, and more particularly to an algorithm for detecting tumors in computerized axial tomography scan data.

BACKGROUND

[0004] Computerized axial tomography scans may be used to obtain images of internal organs of patients, and such images may show indications of abnormalities, such as tumors, in a patient. These indications may be subtle, however, resulting in a risk of failing to diagnose abnormalities that are present, or of incorrectly concluding that abnormalities are present in a normal organ.

[0005] Thus, there is a need for an improved system and method of detecting tumors. SUMMARY

[0006] Aspects of embodiments of the present disclosure are directed toward a system and method for detecting tumors. Three-dimensional scans of a patient are performed with penetrating radiation, before and/or after the injection of a contrast agent. Raw density arrays are formed from the scans. The median density within an organ is calculated and subtracted from each of the raw density arrays, to form offset arrays. The offset arrays are subtracted pairwise and the differences are summed to form a discriminator array.

[0007] According to an embodiment of the present invention there is provided a method for detecting a tumor, the method including: determining, in each of a plurality of raw density arrays, a respective median value within a region of the respective raw density array, each of the plurality of raw density arrays being a three dimensional array having a plurality of array elements, each of the raw density arrays being associated with a respective point in time, each element of each of the raw density arrays representing a density of a portion of a patient at the respective point in time; forming a plurality of offset density arrays, each corresponding to a respective one of the raw density arrays, the forming of the offset density arrays including subtracting, from each of the raw density arrays, the respective median value; forming a first difference array, the forming of the first difference array including subtracting, from a first offset density array, a second offset density array, the second offset density array being associated with a later point in time than the first offset density array; forming a second difference array, the forming of the second difference array including subtracting, from a third offset density array, a fourth offset density array, the fourth offset density array being associated with a later point in time than the third offset density array; and forming a discriminator array, the forming of the discriminator array including adding the first difference array and the second difference array.

[0008] In one embodiment, the method includes receiving the plurality of density arrays, each of the plurality of density arrays being an array of density values representing radiographic density in the patient.

[0009] In one embodiment, the forming of the discriminator array further includes replacing with zero any value that is less than zero, in each of the first difference array and the second difference array.

[0010] In one embodiment, the first offset density array is the same as the third offset density array.

[0011] In one embodiment, the method includes forming a plurality of difference arrays including the first difference array and the second difference array and including a difference array for every pair of offset density arrays, each of the difference arrays being formed by subtracting, from an earlier offset density array, a later offset density array, the later offset density array being associated with a later point in time than the earlier offset density array.

[0012] In one embodiment, the method includes an earliest raw density array associated with an earliest point in time and three later raw density arrays each associated with a point in time later than the earliest point in time, the three respective points in time associated with the three later raw density arrays being separated by about 30 seconds.

[0013] In one embodiment, the method includes: performing a first computerized axial tomography scan on a patient to obtain a first raw density array of the plurality of raw density arrays; injecting a contrast agent into the patient; and performing a second computerized axial tomography scan on the patient, about 30 seconds after injecting the contrast agent into the patient to obtain a second raw density array of the plurality of raw density arrays.

[0014] In one embodiment, the object is a patient and the region corresponds to an organ of the patient.

[0015] In one embodiment, the forming the discriminator array further includes setting to zero each element corresponding to a voxel that is not in the organ. [0016] In one embodiment, the method includes displaying a two dimensional view of the discriminator array on a display.

[0017] In one embodiment, the forming the discriminator array further includes setting to zero each element of the discriminator array for which: a corresponding element of a difference array formed by subtracting, from an earliest offset density array of the plurality of offset density arrays, a second-earliest offset density array of the plurality of offset density arrays is less than or equal to zero; and a corresponding element of the second-earliest offset density array is less than 0, wherein the earliest offset density array is formed from a raw density array, of the plurality of raw density arrays, associated with an earliest one of the points in time, and the second-earliest offset density array is formed from a raw density array, of the plurality of raw density arrays, associated with a second-earliest one of the points in time.

[0018] In one embodiment, the forming the discriminator array further includes setting to zero each element of the discriminator array for which, for any of the raw density arrays, a corresponding element has a value greater than an upper threshold or less than a lower threshold.

[0019] According to an embodiment of the present invention there is provided a system for detecting a tumor, the system including: a scanner for scanning the object with penetrating radiation and measuring the transmission of the penetrating radiation through a patient; a processing circuit; and a display, the processing circuit being configured to: determine, in each of a plurality of raw density arrays, a respective median value within a region of the respective raw density array, each of the plurality of raw density arrays being a three dimensional array having a plurality of array elements, each of the density arrays being associated with a point in time, each element of each of the raw density arrays representing a density of a portion of a patient at the respective point in time; form a plurality of offset density arrays, each corresponding to a respective one of the raw density arrays, the forming of the offset density arrays including subtracting, from each of the raw density arrays, the respective median value; form a first difference array, the forming of the first difference array including subtracting, from a first offset density array, a second offset density array, the second offset density array being associated with a later point in time than the first offset density array; form a second difference array, the forming of the second difference array including subtracting, from a third offset density array, a fourth offset density array, the fourth offset density array being associated with a later point in time than the third offset density array; and form a discriminator array, the forming of the discriminator array including adding the first difference array and the second difference array.

[0020] In one embodiment, the forming of the discriminator array further includes replacing with zero any value that is less than zero, in each of the first difference array and the second difference array. [0021] In one embodiment, the first offset density array is the same as the third offset density array.

[0022] In one embodiment, the system includes the first difference array and the second difference array and including a difference array for every pair of offset density arrays, each of the difference arrays being formed by subtracting, from an earlier offset density array, a later offset density array, the later offset density array being associated with a later point in time than the earlier offset density array.

[0023] In one embodiment, the system includes an earliest raw density array associated with an earliest point in time and three later raw density arrays each associated with a point in time later than the earliest point in time, the three respective points in time associated with the three later raw density arrays being separated by about 30 seconds.

[0024] In one embodiment, the object is a patient and the region corresponds to an organ of the patient.

[0025] In one embodiment, the forming the discriminator array further includes setting to zero each element corresponding to a voxel that is not in the organ.

[0026] In one embodiment, the forming the discriminator array further includes setting to zero each element of the discriminator array for which: a corresponding element of a difference array formed by subtracting, from an earliest offset density array of the plurality of offset density arrays, a second-earliest offset density array of the plurality of offset density arrays is less than or equal to zero; and a corresponding element of the second-earliest offset density array is less than 0, wherein the earliest offset density array is formed from a raw density array, of the plurality of raw density arrays, associated with an earliest one of the points in time, and the second-earliest offset density array is formed from a raw density array, of the plurality of raw density arrays, associated with a second-earliest one of the points in time.

BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Features, aspects, and embodiments are described in conjunction with the attached drawings, in which:

[0028] FIG. 1 is a system for detecting tumors, according to an embodiment of the present invention;

[0029] FIG. 2 is a flow chart of a method for detecting tumors, according to an embodiment of the present invention;

[0030] FIG. 3A is an equation showing pairwise differences, arranged in a matrix, according to an embodiment of the present invention;

[0031] FIG. 3B is an equation showing pairwise differences, arranged in a matrix, according to an embodiment of the present invention; [0032] FIG. 4A is a graph of density as a function of time, according to an embodiment of the present invention; and

[0033] FIG. 4B is a graph of density as a function of time, according to an embodiment of the present invention.

DETAILED DESCRIPTION

[0034] The detailed description set forth below in connection with the appended drawings is intended as a description of exemplary embodiments of a system and method for detecting tumors provided in accordance with the present invention and is not intended to represent the only forms in which the present invention may be constructed or utilized. The description sets forth the features of the present invention in connection with the illustrated embodiments. It is to be understood, however, that the same or equivalent functions and structures may be accomplished by different embodiments that are also intended to be encompassed within the spirit and scope of the invention. As denoted elsewhere herein, like element numbers are intended to indicate like elements or features.

[0035] A computerized axial tomography (CAT or CT) scan is a procedure in which an object (e.g., a patient) is illuminated from several directions with penetrating (e.g., X-ray) radiation from a radiation source, and a raw scan image of the transmitted radiation is formed, in each instance, by a detector, to form a plurality of raw scan images, each of which may be represented as a two-dimensional array. The radiation may be attenuated at different rates in different kinds of matter; accordingly, each point in each image may correspond to a transmitted radiant intensity depending on the attenuation rates of the compositions of matter on the path along which the radiation traveled from the radiation source to the detector. From the combination of raw scan images a three-dimensional model of the "density", or "radiographic density", of the object may be formed, where, as used herein with respect to CAT scans, the "density" refers to the local rate of attenuation of the penetrating radiation. The density may be represented, for example, in Hounsfield units. Although examples are discussed in the present disclosure in the context of CAT scans of a human patient, the invention is not limited thereto, and in some embodiments other kinds of scans providing three-dimensional density data, such as magnetic resonance imaging scans, or positron emission tomography scans, or scans of objects other than human patients may be processed in an analogous fashion. In other embodiments, for example, another scanning method, that generates scans that are spatially registered in 3 dimensions to a sub-voxel level is used. In the case of other kinds of scans, density may be defined accordingly; in the case of a positron emission tomography scan, for example, the density may be the density of nuclei that decay by beta plus emission. As used herein, the term "object" includes anything that may be scanned, and encompasses without limitation human patients, animals, plants, inanimate objects, and combinations thereof. [0036] When the object being scanned is a human patient (or other living object), a contrast agent may be used (e.g., injected into or ingested by the patient) to selectively alter the density of some tissues. The contrast agent may for example include a relatively opaque substance (i.e., relatively opaque to the penetrating radiation). The density of tissue containing the contrast agent may be increased as a result, and it may be increased to an extent that depends on the concentration of contrast agent in the tissue. FIG. 1 shows a block diagram of a system for performing a scan and processing and displaying the results, according to one embodiment. The system includes a scanner 110, a processing circuit 115 (described in further detail below), a display 120 for displaying images, or sequences of images in the form of a movie, and one or more input devices 125 such as a keyboard or mouse, that an operator (e.g., a radiologist) may use to operate the system, and to set parameters affecting the processing of the images to be displayed. It should be noted that the processing circuit 115, display 120, and input devices 125 could be part of a unitary system or could be a distributed system, with the processing circuit 115, for example, being separate and communicatively coupled to the display 120 and input devices 125. In some embodiments servers store the images and clients request the stored images, with image processing performed on the server or on the client, or both.

[0037] A plurality of scans may be performed, and analyzed together. For example, a first scan may be performed before the contrast agent is injected, and several subsequent scans may be performed at various times (e.g., at regular intervals, such as 30-second intervals) after injection of the contrast agent, as the concentration of contrast agent changes. The rate at which the concentration of contrast agent increases initially, the peak concentration reached, and the rate at which the concentration of contrast agent subsequently decreases all may depend on the type of tissue.

[0038] In some embodiments, various methods may be employed to generate images from CAT scan data to aid in the use of a CAT scan as a diagnostic tool. A sequence of steps, or "acts", illustrated in FIG. 2 and discussed in further detail below, may be used, for example, to enhance the difference in appearance between normal tissue and cancerous tissue (i.e., a malignant tumor). The process is illustrated, for a patient's liver, in FIG. 2 and in the sample code of Listing 1, below. In other embodiments, an analogous approach may be used to identify cancerous tissue in other organs.

[0039] Listing 1 shows MATLAB™ code for generating a discriminator array that may be used to detect a tumor, i.e., to determine whether a tumor is present in a patient. The code of Listing 1 receives, as input, the variables, al, a2, a3, and a4, each of which contains a "raw density array" (e.g., a first raw density array, a second raw density array, a third raw density array, and a fourth raw density array). Each element of each raw density array represents the density of tissue in a small volume or "voxel" at a physical location in the patient corresponding to the coordinates of the element. [0040] These (three-dimensional) raw density arrays may be generated (to then be received, in act 205 (FIG. 2)) from the (two-dimensional) raw scan images using, e.g., a deconvolution process to infer the density, in three dimensions, of the object (e.g., the patient) being scanned. The first variable, al, may include data from a scan performed before a contrast agent was injected into the patient, and the remaining three files may include data from scans performed at intervals (e.g., 30 second intervals) after the contrast agent was injected into the patient. However, it should be understood that other timing intervals, or a different number of images, may be used.

[0041] In an act 210, the median, over the voxels corresponding to the patient's liver, within each of the raw density arrays, is calculated, and in an act 215 the median is subtracted from the respective raw density array. This act may be implemented, for example, by the code of lines 3-9 of Listing 1. In Listing 1, liver mask is a mask that identifies voxels that are within the patient's liver. The liver mask is a list of element positions in a linearized representation of the raw density arrays. The variables al, a2, a3, and a4 represent (as mentioned above) the raw density arrays, and aim, a2m, a3m, and a4m are "offset density arrays", obtained by subtracting the respective medians, as shown in Listing 1. Each of the raw density arrays and each of the offset density arrays has associated with it a point in time at which the scan that resulted in the raw density array or the offset density array was performed. As such, within any pair of raw density arrays, one, the "earlier" raw density array resulted from a scan performed at an earlier time than the other, which may be referred to as the "later" raw density array. As such, the adjectives such as "earliest", "second- earliest", "earlier", and "later", when used herein to qualify a raw density array or an offset density array refer to the time at which the scan that resulted in the respective array was performed.

[0042] In an act 220, (implemented in lines 18-25 of Listing 1), difference arrays are formed by subtracting the offset density arrays from each other pair-wise. Each possible difference array is formed, so that for four scans (and, accordingly, for four offset density arrays), six difference arrays (i.e., four choose two or 4!/(2! 2!) difference arrays) are formed. These difference arrays are stored, as a result of the execution of lines 20-25 of Listing 1, in the six variables sl2, sl3, sl4, s23, s24, and s34. Each difference array is the result of subtracting, from an earlier offset density array (i.e., an offset density array resulting from an earlier-performed scan), a later offset density array (i.e., an offset density array resulting from a later-performed scan). In some embodiments, more or fewer than four scans are used. In such a case more or fewer than six difference arrays may be calculated; if all possible pairwise differences are calculated, the number of difference arrays may be n choose 2 (i.e., n!/(n! 2!)) if n scans are used.

[0043] Conceptually, the six elements corresponding to a voxel in each of the six difference arrays may be considered to correspond to the lower triangular portion of an antisymmetric matrix (or "skew-symmetric" matrix) examples of which are illustrated in FIG. 3A (for tumorous tissue) and FIG. 3B (for normal tissue). In FIG. 3A, each T(i) is a value from one voxel of the offset density array from the 1 ^th scan of a sequence of four scans as described above, where the voxel is within tumorous tissue. In FIG. 3B, each N(i) is a value from one voxel of the offset density array from the 1 ^th scan of a sequence of four scans as described above, where the voxel is within normal tissue. As used herein, the "lower triangular portion" of a square matrix refers to the elements below the diagonal. Each off- diagonal element at coordinates (i, j) is calculated by subtracting a value from the 1 ^th scan from a value from the j ^th scan, where the values may be two voxels from the same positions in the offset density arrays corresponding to the two scans (as illustrated in FIGs. 3 A and 3B), or the entire raw density arrays (as is the case for the code of lines 20-25 of Listing 1). The matrix of FIG. 3 A is numerically not precisely skew-symmetric because of rounding error.

[0044] FIGs. 4A and 4B show similar results graphically, each being a graph of density in Hounsfield units as a function of time. Raw scan data are shown in FIG. 4A, and offset data (i.e., density after subtraction of the median value for the liver) are shown in FIG. 4B. In a liver consisting primarily of normal tissue, the median density may correspond to the density of normal tissue; accordingly, subtracting the median value (the result of which is shown in FIG. 4B) may result in little variation in the offset data for normal tissue, and the variation, in the offset data, may be more pronounced for tumorous tissue than it is in the raw data.

[0045] A floor may then be set at zero, in an act 225; in each of the difference arrays (each of which includes elements corresponding to one of the elements of the lower triangular portion of a matrix such as that of FIG. 3 A or of FIG. 3B), elements that are less than zero may be replaced with zero. In an act 230, the difference arrays (after the setting of the floor at zero) are summed. This summation is implemented on line 34 of Listing 1 (line 34 begins with "imgData = " and ends with "..."). The sum may be a tumor indicator, or a discriminator array, i.e., a result that generally takes a relatively high value for voxels within tumorous tissue and a relatively low value for voxels in normal tissue.

[0046] Further steps may be taken to improve the ability of the discriminator to produce images in which tumorous tissue is highlighted while reducing the likelihood of generating false alarms, i.e., the likelihood of highlighting normal tissue. For example, as shown in lines 11-16 and 35, a data masking array (data mask in Listing 1), in which each element has (i) a value of zero if the corresponding element of any of the raw density arrays has a value exceeding an upper threshold or less than a lower threshold and (ii) a value of one otherwise, may be formed and multiplied (as, e.g., in line 35 of Listing 1) by the discriminator array. The upper and lower thresholds may be set to correspond to the range of densities expected for the organ of interest, and this method may be used to identify voxels that are not part of the organ of interest. For example, if due to an error in the definition of a mask for the liver (liver mask in Listing 1) some bone is present in the region defined by the liver mask, and if bone has a significantly higher density than liver tissue, then the raw density arrays may have values exceeding the upper threshold in the elements corresponding to voxels containing bone, and the corresponding elements of the data masking array may be set to zero, resulting in the corresponding elements of the discriminator array being set to zero.

[0047] A pattern mask (pattern mask in Listing 1) may further be used to set to zero elements that have characteristics indicating a likelihood of false alarm. Listing 1 shows a test for such characteristics on line 29. Each element of the pattern mask is (i) set to zero when the corresponding element of the difference array formed by subtracting the second-earliest offset density array from the earliest offset density array has a value less than or equal to zero and the corresponding element of the second-earliest offset density array has a value less than zero, and (ii) set to one otherwise. This pattern mask is used in line 35 of Listing 1 to set to zero each element of the discriminator array for which the pattern mask is zero.

[0048] The discriminator array may be displayed to an operator (e.g., a radiologist) who may then make a determination regarding whether a tumor appears to be present in the patient. The discriminator array may be displayed as a moving display that simulates the patient being rotated in front of the operator, with the image of the patient being partially transparent and with the discriminator being displayed, for example, as the intensity of one color (e.g., red) in a three-color (e.g., red, green blue) display. The code of Listing 2 (which calls the ordfilt3 function defined in Listing 3) generates an example of such a moving display. In other embodiments, the discriminator array (a three dimensional array) may be displayed as a sequence of two-dimensional slices. The code of Listing 4 (which also calls the ordfilt3 function defined in Listing 3) generates an array of such slices, in an exemplary embodiment.

Listing 1

I % Core cumulative subtraction matrix algorithm

2

3 % First remove the liver median from each scan

4 % Since the median is an estimate of a healthy liver tissue value,

5 % this should "flatten" the response of healthy tissue

6 aim = al - median(al(liver_mask));

7 a2m = a2 - median(a2(liver_mask));

8 a3m = a3 - median(a3(liver_mask));

9 a4m = a4 - median(a4(liver_mask));

10

II % data mask is a logical array that will discard (0) anything outside of

12 % expected values for the liver

13 data_mask = al>mask_lower_scan_thr & al<mask_upper_scan_thr & ...

14 a2>mask_lower_scan_thr & a2<mask_upper_scan_thr & ...

15 a3>mask_lower_scan_thr & a3<mask_upper_scan_thr & ...

16 a4>mask_lower_scan_thr & a4<mask_upper_scan_thr;

17

18 % compute all 6 combinations of early time minus late time

19 % CSMA lower triangular

20 sl2 = alm-a2m;

21 sl3 = alm-a3m;

22 sl4 = alm-a4m;

23 s23 = a2m-a3m;

24 s24 = a2m-a4m;

25 s34 = a3m-a4m;

26

27 % pattern mask is a logical that will zero out certain patterns that were

28 % deemed to be false alarms

29 pattern_mask = single(sl2 > 0) + single(sl2 <= 0).*single(a2m < 0);

30

31 % final result accumulates the greater of 0 and the 6 matrix terms and also

32 % applies the masks (liver mask is passed in and is 1 inside the liver, and

33 % 0 outside the liver)

imgData

34 (max(sl2,0)+max(sl3,0)+max(sl4,0)+max(s23,0)+max(s24,0)+max( s34,0)) ...

35 .*single(liver_mask).*data_mask.*pattern_mask; Listing 2

1 % Make a gray 3D image (movie) using scans and overlay with various methods

2 % of cancer detection in Red

3

4 crop_to_liver_en = 0;

5 erode_livermask_en = 1;

6 mask_lower_scan_thr = 900;

7 mask_upper_scan_thr = 1200;

8

9 sub_fract = 1.0;

10 fudge=20;

11 bias = fudge-50 - (l-sub_fract)*1000;

12

13 cl = 1;

14 c2 = 1;

15 c3 = 1;

16 gain = 0.333;

17

18 G = 11.25 * 1.5; % 0.5=low, l=medium, 1.5=high, 2=very high

19

20 ROTXY = 1;

21 ROTXZ = 2;

22 ROTYZ = 3;

23

24 rot_axis = ROTXY;

25

26 dirname = 'H:\Apollo\data\ROC Curve Data\';

27

28 if ~exist('c_type','var')

29 c_type = input('c_type (hcc, pancreas_data):','s');

30 end

31

32 if ~exist('patient_num','var')

33 patient_num = inputfPatient number:');

34 end

35

36 fname = strcat( dirname,c_type,'\',num2str(patient_num),'_imgsetl.mat'); 37 load(fname) % Peter's liver masks

loadistrcatidirname^^ype XlivermaskJ^^ype J^umZstripatien^numJ .mat')) if erode_livermask_en

liver_mask = bw_eroded;

else

liver_mask = bw;

end al = single(imgsetl(l). image);

a2 = single(imgsetl(2). image);

a3 = single(imgsetl(3). image);

a4 = single(imgsetl(4). image); if max(liver_mask(:)) > 0 && size(al,3)==size(liver_mask,3) %

%

core_csma

%

% e = imgData/600; e = ordfilt3( e, 5,5,3);

% Square decompresses covariance values but still in -1 to 1 range

e = e. ^A 2; % .* sign(imgData); grscl = a2-sub_fract*al; bkg = mean(grscl(liver_mask==l)); a21 = grscl + G*e/0.3*bkg;

a31 = grscl;

a41 = grscl; idx_offset=0; 76

77 liv_cut_x = max(squeeze(max(liver_mask,[],3)),[],2);

78 x_idx_liv = find(liv_cut_x > 0.5);

79 liv_cut_y = max(squeeze(max(liver_mask,[],3)),[],l);

80 yjdxjiv = find(liv_cut_y > 0.5);

81 liv_cut_z = max(squeeze(max(liver_mask,[],l)),[],l);

82 z_idx_liv = find(liv_cut_z > 0.5);

83

84 if crop_to_liver_en

85 a21 = a21( x idx liv, y idx liv, z idx liv);

86 a31 = a31( xjdxjiv, yjdxjiv, zjdxjiv);

87 a41 = a41( x idx liv, y idx liv, z idx liv);

88 liver_mask = liver_mask( x_idx_liv, y_idx_liv, z_idx_liv);

89 end

90

91 red4 = max(a21+bias,eps).*liver_mask;

92 grn4 = max(a31+bias,eps).*liver_mask;

93 blu4 = max(a41+bias,eps).*liver_mask;

94

95 red5 = red4;

96 grn5 = grn4;

97 blu5 = blu4;

98

99 [Nx,Ny,Nz] = size(red5);

100

101 if rot_axis == ROTXZ

102 mask = ones(Nx,l) * (1:Νζ). ^Λ 2;

103 elseif rot_axis == ROTYZ

104 mask = ones(Ny,l) * (1:Νζ). ^Λ 2;

105 elseif rot_axis == ROTXY

106 mask = (Νχ:-1:1)'. ^Λ 3/Νχ ^Λ 2 * ones(l,Ny);

107 end

108

109 ANG_LIMIT = 90;

110

111 ANG_STEP = round( AN G_LI MIT/45);

112

113 ang_array = [(0:-ANG_STEP:-ANG_LIMIT),... 114 (-ANG_LIMIT+1:ANG_STEP:ANG_LIMIT),(ANG_LIMIT-1:-ANG_STEP:1)] ;

115

116 clear col_im

117 clear Mov

118

119 img_count = 0;

120

121 for ang = 0 %ang_array

122

for n = 1 Ny*(rot_axis==ROTXZ) + Nx*(rot_axis==ROTYZ) +

123 Nz*(rot_axis==ROTXY)

124

125 if rot_axis == ROTXZ

126 red_temp = squeeze( red4(:,n,:) );

127 grn_temp = squeeze( grn4(:,n,:) );

128 blu_temp = squeeze( blu4(:,n,:) );

129 elseif rot_axis == ROTYZ

130 red_temp = squeeze( red4(n,:,:) );

131 grn_temp = squeeze( grn4(n,:,:) );

132 blu_temp = squeeze( blu4(n,:,:) );

133 elseif rot_axis == ROTXY

134 red_temp = squeeze( red4(:,:,n) );

135 grn_temp = squeeze( grn4(:,:,n) );

136 blu_temp = squeeze( blu4(:,:,n) );

137 end

138

139 red_tempr=imrotate(red_temp,ang,'nearest','crop');

140 grn_tempr=imrotate(grn_temp,ang,'nearest','crop');

141 blu_tempr=imrotate(blu_temp,ang,'nearest','crop');

142

143 if rot axis == ROTXZ

144 red5(:,n,:) = red_tempr .* mask;

145 grn5(:,n,:) = grn_tempr .* mask;

146 blu5(:,n,:) = blu_tempr .* mask;

147 elseif rot axis == ROTYZ

148 red5(n,:,:) = red_tempr .* mask;

149 grn5(n,:,:) = grn_tempr .* mask;

150 blu5(n,:,:) = blu_tempr .* mask; 151 elseif rot_axis == ROTXY

152 red5(:,:,n) = red_tempr .* mask;

153 gm5(:,:,n) = grn_tempr .* mask;

154 blu5(:,:,n) = blu_tempr .* mask;

155 end

156 end

157

158 sizl = 512; %size(red5,l); %max( size(red5,l), 886/2); %886;

159

160 if rot_axis == ROTXZ | | rot_axis == ROTYZ

161 red_im = squeeze(cl*mean(red5,3)+c2*max(red5,[],3)+c3*std(red5,[],3)) ;

162 grn_im = squeeze(cl*mean(grn5 )+c2*max(grn5,[],3)+c3*std(grn5,[],3));

163 blu_im = squeeze(cl*mean(blu5,3)+c2*max(blu5,[],3)+c3 ^5|c std(blu5,[],3));

164 fctr = sizl/(size(red_im,l));

165 elseif rot_axis == ROTXY

166 red_im = squeeze(cl*mean(red5,l)+c2*max(red5,[],l)+c3 ^5|c std(red5,[],l))';

167 grn_im = squeeze(cl*mean(grn5,l)+c2*max(grn5,[],l)+c3*std(grn5,[],l)) ';

168 blu_im = squeeze(cl*mean(blu5,l)+c2*max(blu5,[],l)+c3 ^5|c std(blu5,[],l))';

169 fctr = sizl/(size(red_im,l)*2.5);

170 end

171

172 siz2 = round(fctr*size(red_im,2));

173 if siz2 > 1700

174 siz2 = 1700;

175 sizl = round(siz2*(size(red_im,l)*2.5)/size(red_im,2));

176 end

177 col_im(:,:,l) = imresize(red_im,[sizl,siz2], 'bilinear');

178 col_im(:,:,2) = imresize(grn_im,[sizl,siz2], 'bilinear');

179 col_im(:,:,3) = imresize(blu_im,[sizl,siz2], 'bilinear');

180 denom = mean(blu_im(blu_im>eps));

181

182 image(max(0,min(l,gain*col_im/denom)))

183 if img_count==0,truesize;end

184

185 img_count = img_count + 1;

186

187 axis('image','off)

188 189 drawnow

190 Mov(img_count)=getframe;

191

192 end

193

194

195 fname_avi = strcat( fname(l:strfind(fname, ^, _imgsetl')),c_type, ^, _csma_3D.avi'); 196

197 movie2avil( Mov, fname_avi, 30)

198

199 end Listing 3

1 function a_filt = ordfilt3( a, NFILTx, NFILTy, NFILTz )

2

3 %median filter in x-y

4 %min filter in z

5

6 xyfilt_order = 0.5; %0.5 = median

7

8 a_filt = a;

9

10 [~ ~ Nz] = size(a);

11

12 for z = l : Nz

13

14 if NFILTx>l | | NFILTy>l

a_filt(:,:,z)

15 ordfilt2(squeeze(a(:,:,z)),round(NFILTx*NFILTy*xyfilt_order) ,ones(NFILTx,NFILTy));

16 end

17

18 end

19

20 temp = a_filt;

21

22 if NFILTz>l

23 for z = (NFILTz+l)/2 : Nz-(NFILTz-l)/2

24 a_filt(:,:,z) = median( temp(:,:,z-(NFILTz-l)/2:z+(NFILTz-l)/2),3); 25 % a_filt(:,:,z) = min( temp(:,:,z-(NFILTz-l)/2:z+(NFILTz-l)/2),[],3);

26 end

27 end

Listing 4

1 % clear

2 % close all

3

4 % Magic numbers

5 erode_livermask_en = 1;

6 mask_lower_scan_thr = 900;

7 mask_upper_scan_thr = 1200;

8 disp_lower_scan_thr = 900;

9 disp_upper_scan_thr = 1300;

10 scale_factorl = 0.5;

11 scale_factor2 = 0.375;

12 imgData_norm = 600;

13 L_med_x = 5;

14 L_med_y = 5;

15 L_med_z = 3;

16 e_power = 2;

17 contrast_setting = 1.5; %0.5=LO, 1=MED, 1.5=HI, 2= VHI

18 G = 100 * contrast_setting;

19

20 if ~exist('c_type','var')

21 c_type = input('c_type (hcc, pancreas_data):','s');

22 end

23

24 if ~exist('patient_num','var')

25 patient_num = input('Patient number:');

26 end

27

28 dirname = 'h:\apollo\data\ROC Curve Data\';

29

30 fname = strcat( dirname,c_type,'\',num2str(patient_num),'_imgsetl.mat');

31 load(fname)

32

33 clear bw clear BW load(strcat(dirname,c_type \livermaskJ,c_type J,num2str(patient_num) .mat')) if erode_livermask_en

liver_mask = bw_eroded;

else

liver_mask = bw;

end num_scans = length(imgsetl);

num_slices = size(imgsetl(l).image,3); % fprintf('Number of scans = %i\n',num_scans)

% fprintf('Number of slices = %i\n',num_slices) al = single(imgsetl(l). image);

a2 = single(imgsetl(2). image);

a3 = single(imgsetl(3). image);

a4 = single(imgsetl(4). image); if length(liver_mask(:)) == length(al(:)) %

%

core_csma

%

% %

%

%

% DISPLAY CUBE CODE below

%

%

% e = imgData/imgData_norm; 72 e = ordfilt3( e, L_med_x,L_rned_y,L_med_z);

73 e = e. ^A e_power;

74

grscl max(0,min(disp_upper_scan_thr-disp_lower_scan_thr,a2- disp_lower_scan_thr));

76

77 bkg = mean(grscl(liver_mask==l));

78

79 % add "e" to red only

80 a21 = grscl + G*e*bkg;

81 a31 = grscl;

82 a41 = grscl;

83

84 red4 = max(a21,eps) .*liver_mask;

85 grn4 = max(a31,eps) .*liver_mask;

86 blu4 = max(a41,eps) .*liver_mask;

87

88 data = zeros(size(a21,l),size(a21,2),size(a21,3),3,'single');

89 norm_fact = 3*median(grn4(liver_mask == 1));

90

91 data(:, :, :,l) = red4/norm_fact*scale_factor2;

92 data(:, :, :,2) = grn4/norm_fact*scale_factor2;

93 data(:, :, :,3) = blu4/norm_fact*scale_factor2;

94

95 csma = uint8( round( 255*min(l,max(0,data))));

96

fname_mat = strcat(

97 fname(l:strfind(fname,'_imgsetl')),c_type,'_csma_4D.mat');

98

99 save(fname_mat,'csma')

100

101 else

102

103 fprintffError: Liver mask has z dimension %i\n',size(liver_mask,3))

104 end

[0049] The term "processing circuit" is used herein to include any combination of hardware, firmware, and software, employed to process data or digital signals. Processing circuit hardware may include, for example, application specific integrated circuits (ASICs), general purpose or special purpose central processing units (CPUs), digital signal processors (DSPs), graphics processing units (GPUs), and programmable logic devices such as field programmable gate arrays (FPGAs). In a processing circuit, as used herein, each function is performed either by hardware configured, i.e., hard-wired, to perform that function, or by more general purpose hardware, such as a CPU, configured to execute instructions stored in a non-transitory storage medium. A processing circuit may be fabricated on a single printed wiring board (PWB) or distributed over several interconnected PWBs. A processing circuit may contain other processing circuits; for example a processing circuit may include two processing circuits, an FPGA and a CPU, interconnected on a PWB. A processing circuit may include a plurality of processing units that are geographically separated, and connected, e.g., by a network such as the internet.

[0050] Although limited embodiments of a system and method for detecting tumors have been specifically described and illustrated herein, many modifications and variations will be apparent to those skilled in the art. Accordingly, it is to be understood that the system and method for detecting tumors employed according to principles of this invention may be embodied other than as specifically described herein. The invention is also defined in the following claims, and equivalents thereof.