3D Ultrasonic Diagnosis of Breast Tumors. Wei-Ming Chen

Transcription

1 3D Ultrasonic Diagnosis of Breast Tumors Wei-Ming Chen

2 Three major benefits of ultrasound Ultrasound imaging has been shown to be valuable for differentiating some aspects of benign and malignant diseases. More convenient and safe tool for classifying the breast tumor. The equipment of the ultrasound image also is far less expensive to buy.

3 2D US image Benign Case Malignant Case

4 The drawback of the diagnosis using 2D ultrasound Images The relationship between surface features and internal architecture of images of the breast tumor is not very easy to be revealed in 2D imaging. An atrocity will happen when the 2D plane is not scanned at the correct piece of the tumor. The 2D image may only contain the partial texture features of the tumor.

5 Feature of 3D ultrasound images The malignant tumor of the breast has the characteristics of uneven shape and complex texture in three dimensions. Calculating the tumor volume within three dimensional ultrasound data can increase the confidence level of clinical diagnosis. The 3D ultrasound images can improve the performance over it by exploiting the correlation between the whole tumor in three dimensions. The quality of 3D ultrasound breast images is limited by the ultrasound scanning equipment due to imagereconstruction.

6 The goal of this research 3D ultrasound imaging technique. 3D texture extraction. Computer Aided Diagnosis (CAD) Systems for differentiating diagnosis.

7 Four proposed algorithms of diagnosis upon the 3D ultrasound images 2D accumulative auto-correlation scheme (ACC-US) / (ACC5-US) 2D multiple auto-correlation scheme (MULTI-US) 3D auto-correlation scheme (AC-3DUS) 3D run difference matrix scheme (RDM-3DUS) / (AC-RDM-3DUS)

8 Data acquisition system Scan range: 20-25

9 3D-view view-2000 software

10 3D volume in 3D-view view-2000 software

11 3D ultrasound data in Cartesian coordinates First 2D-scan Inner 2D-scan Last 2D-scan Range of sweep

12 3D volume file data structure File Header Frame 1 Frame Frame N-1 Frame N

13 The information of the 3D volume database sorted by size SerNO 3D Volume Name Tumor size in diameter (cm) Pixel-Rate (Pixels/cm) 1 \NOCANCER\yoo-kyungok.v \NOCANCER\park-soonkeum.v \NOCANCER\yoo-byungsook.v \NOCANCER\yun-giljae.v \NOCANCER\min-duksoon.v \NOCANCER\oh-hyunju.v \NOCANCER\junh-hyonam.v \NOCANCER\sung-kyunghee.v \NOCANCER\whang-yeonhee.v \NOCANCER\jang-jungim.v \NOCANCER\park-myungja.v \NOCANCER\hyen-taesoon.v \NOCANCER\moon-hyunok.v \NOCANCER\park-chanam.v \NOCANCER\cha-hyunkyung.v \CANCER\lee-ockhyun.v \CANCER\jun-ilsoon.v \CANCER\yum-kyungsub.v \CANCER\yun-euisoon.v \NOCANCER\lee-younghee.v \CANCER\kim-myonam.v AVERAGE

14 Diagram of proposed diagnosis system Tumor capture Feature extract Learning machine 3DUS image database (training sets) Training result Diagnosis result Diagnosis machine 3DUS image database (outside sets)

15 Artificial neural network (ANN) input signals input neuron layer synaptic weights hidden neuron layer synaptic weights output neuron layer output signals

16 Pixel relation extraction Pixel relation extraction Normalized auto-correlation coefficients = + = m M x m x f x f m M m A 1 0 ) ( ) ( 1 ) ( (0) ) ( ) ( A m A m c = + = dm m x g m f x g x f ) ( ) ( ) ( ) ( o Auto-correlation Correlation = + = 1 0 ) ( ) ( 1 ) ( ) ( M m m x f m f M x f x f o

17 Normalized 2D auto Normalized 2D auto-correlation coefficients correlation coefficients ( ) ( ) ( ) 0,0,, A n m A n m = γ ( ) ( ) ( ) = = + + = n N y m M x n y m x f y x f n N m M n m A ,, ) )( ( 1, ( ) ( ) ( ) = = + + = n N y m M x f n m y x f f y x f n N m M n m A ), )(, ( ) )( ( 1, ' ( ) ( ) ( ) 0,0 ', ', A n m A n m = γ Mean-removed

18 Volume of interesting (VOI) First plane VOI Middle plane Last plane ROI

19 CAD program using 2D accumulative auto-correlation (ACC-US)

20 2D accumulative auto-correlation method 2D autocorrelation matrix (5 5) 2D autocorrelation matrix (5 5)... 2D accumulative auto-correlation matrix (5 5) 2D autocorrelation matrix (5 5)

21 The structure of ACC-US method 3D Ultrasound diagnosis equipment First_stage 3D Ultrasound images database Extract the ROI in each slice of the 3D volume by the First, Middle and Last planes 2D Accumulative auto-correlation matrix calculation

22 Second stage characteristic vectors (2D normalized autocorrelation matrix) Pre-computed synaptic weights multilayer feed-forward neural network Benign breast tumor (0) or Malignant breast tumor (1) result

23 5 5 auto-correlation matrix = 24 auto-correlation coefficients. (Since the auto-correlation matrix is normalized, γ(0, 0) is always 1) Including a predefined threshold, there are 25 nodes for input layer of ANN. Calibrated expression of the 2D auto-correlation. m = n = Pixel_Rate Auto_correlation_Step

24 2D accumulative scheme learning result Training Set Number of Malignant Cases Number of Benign Cases Number of Iterations Error Distortions 1,2,3,4, ,2,3, ,2,3, ,2,4, ,3,4, ,3,4,

25 Diagnosis measurements TN: The number of benign cases which are diagnosed correctly. FP: The number of benign cases which are misdiagnosed. TP: The number of malignant cases which are diagnosed correctly. FN: The number of malignant cases which are misdiagnosed. Correct(True) T P Diagnosed tumor as malignant Incorrect(False) F N Diagnosed tumor as benign

26 Diagnosis measurements Accuracy = (TP+TN)/(TP+TN+FP+FN) Sensitivity = TP/(TP+FN) Specificity = TN/(TN+FP) Negative Predictive Value = TN/(TN+FN) Positive Predictive Value = TP/(TP+FP)

27 CAD result for ACC-US scheme Case No Case1 Case2 Case3 Case4 Case5 Total OutSide Training or Outside Trai n i n g sets Benign Cases Include d Malignant Cases Include d False Positives (FP) False Negatives (FN) Training sets 1,2,3, Outside set All 1 ~ Training sets 1,2,3, Outside set All 1 ~ Training sets 1,2,4, Outside set All 1 ~ Training sets 1,3,4, Outside set All 1 ~ Training sets 2,3,4, Outside set All 1 ~ All of the outside set 1 ~

28 Measurements of ACC-US method Case No Case1 Case2 Trai n i n g or Outside Trai n i n g sets Accuracy Sensitivity Specificity Negative Predictive Value Positive Predictive Value Training sets 1,2,3, % 100.0% 100.0% 100.0% 100.0% Outside set % 88.9% 91.7% 95.7% 80.0% All 1 ~ % 98.1% 98.1% 99.1% 96.3% Training sets 1,2,3, % 100.0% 100.0% 100.0% 100.0% Outside set % 76.9% 94.7% 85.7% 90.9% All 1 ~ % 94.6% 99.0% 97.2% 98.1% Training sets 1,2,4, % 100.0% 100.0% 100.0% 100.0% Case3 Outside set % 90.9% 95.2% 95.2% 90.9% All 1 ~ % 98.1% 99.1% 99.1% 98.1% Training sets 1,3,4, % 100.0% 100.0% 100.0% 100.0% Case4 Outside set % 77.8% 82.6% 90.5% 63.6% All 1 ~ % 96.2% 96.3% 98.1% 92.6% Training sets 2,3,4, % 100.0% 100.0% 100.0% 100.0% Case5 Outside set % 55.6% 92.9% 61.9% 90.9% All 1 ~ % 86.9% 99.0% 92.5% 98.1% Total outside All of the outside sets 1 ~ % 83.3% 86.0% 91.1% 75.0%

29 2D multiple auto-correlation method (MULTI-US) US) Frame 1 Frame Frame n 2D autocorrelation matrix (5 5) 2D autocorrelation matrix (5 5) 2D autocorrelation matrix (5 5) 2D autocorrelation matrix (5 5)

30 The structure of MULTI-US US method 3D Ultrasound diagnosis equipment First Stage 3D Ultrasound images database Extract the ROI in each slice of the 3D volume by the First, Middle and Last planes Multiple 2D auto-correlation matrix calculation

31 Second Stage characteristic vectors (2D normalized autocorrelation matrixes) Pre-computed synaptic weights multilayer feed-forward neural network Benign breast tumor (0) or Malignant breast tumor (1) result

32 Training Set MULTI-US US scheme learning result Number of Malignant Cases Number of Benign Cases Number of Iterations Error Distortions 1,2,3,4, ,2,3, ,2,3, ,2,4, ,3,4, ,3,4,

33 CAD result for MULTI-US US scheme Case No Case1 Case2 Case3 Case4 Case5 Total OutSide Training or Outside Training sets Benign Cases Included Malignant Cases Included False Positives (FP) False Negatives (FN) Training sets 1,2,3, Outside set All 1 ~ Training sets 1,2,3, Outside set All 1 ~ Training sets 1,2,4, Outside set All 1 ~ Training sets 1,3,4, Outside set All 1 ~ Training sets 2,3,4, Outside set All 1 ~ All of the outside set 1 ~

34 Measurements of MULTI-US US method Case No Case1 Trai n i n g or Outside Trai n i n g sets Accuracy Sensitivity Specificity Negative Predictive Value Positive Predictive Value Training sets 1,2,3,4 94.5% 93.0% 96.4% 96.4% 90.9% Outside set % 66.7% 82.6% 82.6% 80.0% All 1 ~ % 92.3% 96.2% 96.2% 88.9% Training sets 1,2,3,5 91.5% 86.4% 93.0% 93.0% 88.4% Case2 Outside set % 66.7% 76.2% 76.2% 90.9% All 1 ~ % 85.7% 92.3% 92.3% 88.9% Training sets 1,2,4,5 94.6% 95.0% 97.7% 97.7% 88.4% Case3 Outside set % 83.3% 90.5% 90.5% 90.9% All 1 ~ % 92.3% 96.3% 96.3% 88.9% Training sets 1,3,4,5 93.0% 88.6% 94.2% 94.2% 90.7% Case4 Outside set % 71.4% 81.0% 81.0% 90.9% All 1 ~ % 87.5% 93.3% 93.3% 90.7% Training sets 2,3,4,5 95.3% 93.0% 96.5% 96.5% 93.0% Case5 Outside set % 58.3% 76.2% 76.2% 63.6% All 1 ~ % 85.5% 92.5% 92.5% 87.0% Total outside All of the outside sets 1 ~ % 83.3% 81.3% 90.6% 69.2%

35 Accumulative auto-correlation matrix with only five frames(acc5-us) 2D auto-correlation matrix (5 5) Frame n-2.. 2D auto-correlation matrix (5 5) Frame n(middle frame) 2D accumulative auto-correlation matrix (5 5).. 2D auto-correlation matrix (5 5) Frame n+2

36 CAD result for ACC5-US scheme Case No Case1 Case2 Case3 Case4 Case5 Total OutSide Training or Outside Training sets Benign Cases Included Malignant Cases Included False Positives (FP) False Negatives (FN) Training sets 1,2,3, Outside set All 1 ~ Training sets 1,2,3, Outside set All 1 ~ Training sets 1,2,4, Outside set All 1 ~ Training sets 1,3,4, Outside set All 1 ~ Training sets 2,3,4, Outside set All 1 ~ All of the outside set 1 ~

37 Measurements of ACC5-US method Case No Case1 Case2 Training or Outside Training sets Accuracy Sensitivity Specificity Negative Predictive Value Positive Predictive Value Trai n i n g se ts 1,2,3,4 94.5% 93.0% 95.3% 96.4% 90.9% Outside set % 90.9% 100.0% 95.7% 100.0% All 1 ~ % 92.6% 96.3% 96.3% 92.6% Trai n i n g se ts 1,2,3,5 91.5% 86.4% 94.1% 93.0% 88.4% Outside set % 80.0% 86.4% 90.5% 72.7% All 1 ~ % 85.2% 92.5% 92.5% 85.2% Trai n i n g se ts 1,2,4,5 94.6% 95.0% 94.4% 97.7% 88.4% Case3 Outside set % 84.6% 100.0% 90.5% 100.0% All 1 ~ % 92.5% 95.4% 96.3% 90.7% Trai n i n g se ts 1,3,4,5 93.0% 88.6% 95.3% 94.2% 90.7% Case4 Outside set % 76.9% 94.7% 85.7% 90.9% All 1 ~ % 86.0% 95.2% 92.5% 90.7% Trai n i n g se ts 2,3,4,5 95.3% 93.0% 96.5% 96.5% 93.0% Case5 Outside set % 83.3% 95.0% 90.5% 90.9% All 1 ~ % 90.9% 96.2% 95.3% 92.6% Total outside All of the outside sets 1 ~ % 90.7% 90.7% 95.1% 83.1%

38 ROC curves of all 2D CAD

39 Diagnosis to 3D ultrasound images based on 3D auto-correlation (AC-3DUS) 3D autocorrelation matrix (3 3 3 )

40 γ Mean-removed normalized 3D auto- correlation coefficients ( m, n, p) = A' ( m, n, p) A' ( 0,0,0) A' ( M ( m, n, p) = 1 m)( N n)( P p) M 1 m N 1 n P 1 p x= 0 y= 0 z= 0 ( f ( x, y, z) f )( f ( x + m, y + n, z + p) f )

41 3 3 3 auto-correlation matrix = 26 auto-correlation coefficients (Since the auto-correlation matrix is normalized, γ(0, 0, 0) is always 1) Including a predefined threshold, there are 27 nodes for input layer of ANN Calibrated expression of the 3D auto-correlation m = n = p = Pixel_Rate Auto_correlation_Step

42 The structure of AC-3DUS method 3D Ultrasound diagnosis equipment First Stage 3D Ultrasound images database Extract the VOI-array by the first, middle and last frame 3D auto-correlation matrix calculation

43 Second Stage characteristic vectors (3D normalized autocorrelation matrix) Pre-computed synaptic weights multilayer feed-forward neural network Benign breast tumor (0) or Malignant breast tumor (1) result

44 AC-3DUS learning result Training Set Number of Malignant Cases Number of Benign Cases Number of Iterations Error Distortions 1,2,3,4, ,2,3, ,2,3, ,2,4, ,3,4, ,3,4,

45 AC-3DUS method diagnosis result Case No Case1 Case2 Case3 Case4 Case5 Total OutSide Training or Outside Trai n i n g sets Benign Cases Included Malignant Cases Included False Positives (FP) False Negatives (FN) Trai n i n g se ts 1,2,3, Outside set All 1 ~ Trai n i n g se ts 1,2,3, Outside set All 1 ~ Trai n i n g se ts 1,2,4, Outside set All 1 ~ Trai n i n g se ts 1,3,4, Outside set All 1 ~ Trai n i n g se ts 2,3,4, Outside set All 1 ~ All of the outside set 1 ~

46 Measurements of AC-3DUS method Training or Outside Trai n i n g sets Accuracy Sensitivity Specificity Negative Predictive Value Positive Predictive Value Trai n i n g se ts 1,2,3, % 100.0% 100.0% 100.0% 100.0% Outside set % 90.9% 100.0% 95.7% 100.0% All 1 ~ % 98.2% 100.0% 99.1% 100.0% Trai n i n g se ts 1,2,3, % 100.0% 100.0% 100.0% 100.0% Outside set % 80.0% 86.4% 90.5% 72.7% All 1 ~ % 96.2% 97.2% 98.1% 94.4% Trai n i n g se ts 1,2,4, % 100.0% 100.0% 100.0% 100.0% Outside set % 91.7% 100.0% 95.2% 100.0% All 1 ~ % 98.2% 100.0% 99.1% 100.0% Trai n i n g se ts 1,3,4, % 100.0% 100.0% 100.0% 100.0% Outside set % 78.6% 100.0% 85.7% 100.0% All 1 ~ % 94.7% 100.0% 97.2% 100.0% Trai n i n g se ts 2,3,4, % 100.0% 100.0% 100.0% 100.0% Outside set % 83.3% 95.0% 90.5% 90.9% All 1 ~ % 96.4% 99.1% 98.1% 98.1% All of the outside sets 1 ~ % 92.6% 91.6% 96.1% 84.7%

47 Conventional 2D CAD learning result Training Set Number of Malignant Cases Number of Benign Cases Number of Iterations Error Distortions 1,2,3,4, ,2,3, ,2,3, ,2,4, ,3,4, ,3,4,

48 Conventional 2D CAD result Case No Case1 Case2 Case3 Case4 Case5 Total OutSide Training or Outside Trai n i n g sets Benign Cases Included Mali gnant Cases Include d False Positives (FP) False Negatives (FN) Training sets 1,2,3, Outside set All 1 ~ Training sets 1,2,3, Outside set All 1 ~ Training sets 1,2,4, Outside set All 1 ~ Training sets 1,3,4, Outside set All 1 ~ Training sets 2,3,4, Outside set All 1 ~ All of the outside set 1 ~

49 Measurements of CN-US method Case No Case1 Case2 Training or Outside Training sets Accuracy Sensitivity Specificity Negative Predictive Value Positive Predictive Value Training sets 1,2,3,4 93.8% 90.9% 95.2% 95.2% 90.9% Outside set % 72.7% 90.9% 87.0% 80.0% All 1 ~ % 87.3% 94.3% 93.5% 88.9% Training sets 1,2,3,5 91.5% 88.1% 93.1% 94.2% 86.0% Outside set % 70.0% 81.8% 85.7% 63.6% All 1 ~ % 84.6% 90.8% 92.5% 81.5% Training sets 1,2,4,5 92.2% 86.7% 95.2% 93.0% 90.7% Case3 Outside set % 77.8% 82.6% 90.5% 63.6% All 1 ~ % 85.2% 92.5% 92.5% 85.2% Training sets 1,3,4,5 92.2% 88.4% 94.2% 94.2% 88.4% Case4 Outside set % 70.0% 81.8% 85.7% 63.6% All 1 ~ % 84.9% 91.7% 92.5% 83.3% Training sets 2,3,4,5 94.6% 92.9% 95.4% 96.5% 90.7% Case5 Outside set % 47.4% 84.6% 52.4% 81.8% All 1 ~ % 78.7% 94.0% 87.9% 88.9% Total outside All of the outside sets 1 ~ % 70.4% 80.4% 84.3% 64.4%

50 2D/3D auto-correlation method results ACC-US MULTI-US ACC5-US CN-US AC-3DUS Ultrasound Classification Benign Malignant Benign Malignant Benign Malignant Benign Malignant Benign Malignant Benign TN 92 FN 9 TN 87 FN 9 TN 97 FN 5 TN 86 FN 16 TN 98 FN 4 Malignant FP 15 TP 45 FP 20 TP 45 FP 10 TP 49 FP 21 TP 38 FP 9 TP 50 Total

51 Learning program

52 Screen of the diagnostic process

53 Accuracy Sensitivity Specificity PPV NPV CN-US 77.02% 70.37% 80.37% 64.41% 84.31% ACC-US 85.09% 83.33% 85.98% 75.00% 91.09% MULTI-US 82.61% 83.33% 81.31% 69.70% 90.63% ACC5-US 90.68% 90.74% 90.65% 83.05% 95.10% AC-3DUS 91.90% 92.60% 91.60% 84.70% 96.10% % 95.00% 90.00% 85.00% 80.00% 75.00% 70.00% 65.00% Accuracy Sensitivity Specificity PPV NPV CN-US ACC-US MULTI-US ACC5-US AC-3DUS NVP:Negative Predictive Value PPV:Positive Predictive Value

54 Diagnosis to 3D ultrasound images based on run difference matrix (RDM-3DUS) Texture classification methods had been developed and used in biomedical. Gray level histogram Gray level correlation Gray level run length Gray level run difference In ultrasound image, we found that the gray level difference was more effective factor than the run length.

55 Run difference matrix The run difference matrix comprised the gray level difference along with a distance between the pixels when the displacement vector between two pixels was given. Let displacement vector = [Δx, Δy] = [x a, y b ] [x n, y m ]. D It can be represented with distance r and direction θ in polar coordinate as follows: (r) D θ = [ x, y] r = x 2 + y 2,θ = tan 1 x y

56 The RDM (Run Difference Matrix) is defined as a function of r and gray level difference with the given direction θ: RDM(r,g dif ) = # { ( (x n, y m ),(x a, y b ) ) } / N Where (x n,y m ),(x a, y b ) ROI G(x n, y m ) - G(x a, y b ) = g dif G(x, y) is the gray level value of the pixel (x, y) N = # { ( (x n, y m ),(x a, y b ) ) } Where (x n,y m ),(x a, y b ) ROI [x a, y b ] [x n, y m ] = D θ (r)

57 Shows the matrix elements of RDM(r,g,g dif ) Gray level difference g dif Distance r

58 An example of ROI data ROI image

59 The RDM of the sample ROI data g dif r /25 6/25 7/25 3/25 4/25 0/25 2/25 0/25 2 2/20 6/20 7/20 1/20 1/20 1/20 1/20 1/20 3 3/15 3/15 1/15 4/15 2/15 0/15 2/15 0/15 4 1/10 2/10 3/10 2/10 1/10 1/10 0/10 0/10

60 3D run difference matrix 3D run difference matrix For the 3D VOI, it can be represented with distance r and direction θ, λ in polar coordinate as follows: = = + + = = z x y x z y x r z y x r D , tan, tan, ],,, [ ) ( λ θ λ θ

61 The RDM (Run Difference Matrix) is redefined as a function of r and gray level difference with the given direction θ, λ: RDM(r,g dif ) = # { ( (x n, y m, z w ),(x a, y b, z c ) ) } / N Where (x n, y m, z w ),(x a, y b, z c ) ROI G(x n, y m, z w ) - G(x a, y b, z c ) = g dif G(x, y, z) is the gray level value of the pixel (x, y, z) N = # { ( (x n, y m, z w ),(x a, y b, z c ) ) } Where (x n, y m, z w ),(x a, y b, z c ) ROI [x a, y b, z c ] [x n, y m, z w ] = D θ λ, ( r)

62 RDM Characteristic Vector DGD (Distribution of Gray level Difference) vector: DGD( j) r = max r= 1 RDM ( r, DOD (Distribution of Average Difference) vector: j) DOD( r) = n g j= 0 RDM ( r, j) j r : distance j : gray level difference

63 RDM Characteristic parameter LED (Large Difference Emphasis): LED = n g j= 0 SMG (Second Moment of DGD): SMG = j= 0 SMO (Second Moment of DOD): n g DGD( j) ln( K / j) K is a constant DGD( j) 2 SMO r = max r= 1 DOD( r) 2

64 RDM-3DUS method Each case only chose 7 directions. Each RDM produced 3 characteristic parameters. Each case produced 3 7 = 21 coefficients. The dimension r of RDM was 40 pixels in each direction. The dimension g dif of RDM was 256 in each direction.

65 7 feature vectors along 7 directions Z-axis Y-axis X-axis

66 The structure of RDM-3DUS method 3D Ultrasonic diagnosis equipment 3D Ultrasonic images database Extracted the VOI-array by the start, middle and last frame First stage 7 directional run difference matrixes (21 dimension characteristic vectors)

67 Classification of breast nodules by proposed RDM-3DUS method and AC-3DUS method Sonographic Classification RDM-3DUS method AC-3DUS method Benign Malignant Benign Malignant Benign TN 100 FN 6 TN 98 FN 4 Malignant FP 7 TP 48 FP 9 TP 50 Total

68 Performance comparison of the AC-3DUS and RDM-3DUS methods Item Proposed method AC-3DUS p value using chi-square test (RDM-3DUS) (computed with CN-US) Accuracy (%) p<0.001 Sensitivity (%) p<0.005 Specificity (%) p<0.025 Positive Predictive Value (%) p<0.025 Negative Predictive Value (%) p<0.005

69 ROC curves of AC-3DUS and RDM-3DUS methods TPF a=2.32 b=0.74 Az= AC-3DUS a=2.53 b=0.93 Az= RDM-3DUS FPF

70 AC-RDM RDM-3DUS method Y- axis Z- axis X- axis 3D autocorrelation matrix + RDM characteristic parameters

71 Coefficients of AC-RDM RDM-3DUS method Only chose 3 direction. Each RDM produced 3 characteristic parameters. Each case produced 3 3 = 9 parameters. Each 3D auto-correlation matrix consists of 27 parameters = 35 coefficients overall.

72 Classification of breast nodules by proposed AC-RDM RDM-3DUS method and ACC-3DUS method Sonographic Classification AC-RDM-3DUS method AC-3DUS method Benign Malignant Benign Malignant Benign TN 98 FN 3 TN 98 FN 4 Malignant FP 9 TP 51 FP 9 TP 50 Total

73 Performance comparison of the AC-3DUS and AC-RDM RDM-3DUS methods Item Proposed method AC-3DUS p value using chi-square test (AC-RDM-3DUS) (computed with CN-US) Accuracy (%) p<0.001 Sensitivity (%) p<0.005 Specificity (%) p<0.025 Positive Predictive Value (%) p<0.025 Negative Predictive Value (%) p<0.005

74 ROC curves of AC-3DUS and AC-RDM RDM-3DUS methods TPF a=2.32 b=0.74 Az= AC-3DUS a=2.40 b=0.94 Az= AC-RDM-3DUS FPF

75 The diagnostic performance between AC-3DUS, RDM-3DUS and C-RDMC RDM-3DUS methods 98.00% 96.00% 94.00% AC-3DUS 92.00% 90.00% RDM-3DUS 88.00% 86.00% AC-RDM-3DUS 84.00% Accuracy Sensitivity Specificity PPV NPV NVP:Negative Predictive Value PPV:Positive Predictive Value

76 Conclusions Evaluate texture extracting techniques. The 3D US CAD is better than 2D US CAD. The speed of the convergence is faster than the conventional scheme. ACC5-US method got a better performance. RDM is also a effective texture feature for breast diagnosis.

77 Future Works Find some efficient way to fully automate diagnosis of tumors. Capture 2D texture feature from another directions. Combine other diagnosis features for a more reliable CAD. Free hand system. Other classification methods, such as support vector machine (SVM).