In this experiment, there are 146 lesions of 113 patients used for estimating the performance of the proposed tumor detection method. In the stage of false-positive reduction, the binary logistic regression model [34] is adopted with 10-fold cross-validation. The detection results of our proposed method are shown in Table 1.
In this table, the numbers of true-positives (TPs), false-negatives (FNs), and false positives (FPs) are listed for each case. The sensitivity rates for benign and malignant tumors are listed in Table 2. The total number of tumors is 146, in which 67 tumors are malignant and 79 tumors are benign. The sensitivity rate of tumor detection is 89.04% with 4.92 FPs per case. The sensitivity rate for malignant tumors is 94.03%
and the sensitivity rate for benign tumors is 84.1%. The sensitivity rates of different sizes for benign and malignant tumors are listed in Table 3.
For statistical analysis of the proposed features in false-positive reduction, firstly, the Kolmogorov-Smirnov test [35] is applied to observe whether the feature is a normal distribution or not. If the feature is a normal distribution, then the mean values and standard deviation are calculated for the tumors and non-tumors. Differences between the values of the features for the tumors and non-tumors are evaluated with Student’s t test. If the distribution of a feature is not normal, the median value is listed
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and the Mann-Whitney U test [35] is used. A p-value that is less than 0.05 is considered to indicate a statistically significant difference. Our proposed features are determined to be non-normal distributions by the Kolmogorov-Smirnov test. Thus, the Mann-Whitney U test is applied and the median and p-value for respective feature is listed in Table 4. Also, the free-response operating characteristics (FROC) [36] are also adopted to show the performance of our tumor detection system. The FROC, shown in Fig. 9, is generated by the predicted values from the binary logistic regression using different threshold THlogistic. At THlogistic=0.54, the sensitivity rate of tumor detection is 89.04% with 4.92 FPs per case. Note that the number of FPs was 63.32 per case before the false-positive reduction.
According to Table 1, most of the tumors identified by the radiologists could be found through our proposed tumor detection system with lower FP rate per case. The 8 cases of true-positive examples are shown in Fig. 10 - Fig. 17, 3 false-negative cases are shown in Fig. 18 -Fig. 20, and Fig. 21 shows a false-positive example. In these figures, the solid circles indicate the position of the real tumors and the dot circles indicate the FPs after FP reduction.
Table 1 The results for 113 cases with the tumors
Case No. False positive False Negative True positive Benign(0) /Malignant(1)
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20
21
22
99 0 0 1 1
100 11 0 1 0
101 3 0 1 0
102 9 0 1 1
103 2 0 1 1
104 2 0 1 1
105 4 0 1 1
106 3 0 2 0
107 6 0 1 1
108 2 0 1 1
109 3 1 1 1
110 2 0 1 1
111 3 0 1 0
112 3 0 1 0
113 1 0 1 0
Total 556 16 130 B:79/M:67
Table 2 The sensitivity rates of tumor detection for benign and malignant tumors Tumor Number Detected Miss detected Sensitivity
Benign 79 67 12 84.81%
Malignant 67 63 4 94.03%
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Table 3 The sensitivity rate of different sizes for benign and malignant tumors
< 1.0 cm 1.0 – 2.0 cm 2.0 - 3.0 cm ≧3.0 cm
Table 4 Median value and p-value of Mann-Whitney U test for each feature
Feature Median
Long-short axis ratio 3.05 2.07 <0.001*
Variance of radiuses 0.99 1.12 0.19
* The difference was statistically significant.
Fig. 9 The FROC curve of the proposed system.
0%
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Fig. 10 A true-positive case of 1.9 cm infiltrating duct carcinoma. (a) The original image (b) The white areas are the suspicious tumor regions before FP reduction. (c) The white area is the result after FP reduction and the solid circle indicates the position of the real tumor.
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Fig. 11 A true-positive case of 2.0 cm infiltrating duct carcinoma. (a) The original image (b) The white area is the suspicious tumor region before FP reduction.
(c) The white area is the result after FP reduction and the solid circle indicates the position of the real tumor.
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(b)
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Fig. 12 A true-positive case of 1.8 cm fibroadenomas. (a) The original image (b) White areas are the suspicious tumor regions before FP reduction. (c) The white areas are the results after FP reduction. The solid circle indicates the position of the real tumor and the dot circle indicates the FP.
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(a)
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Fig. 13 A true-positive case of 1.3 cm tubular adenoma. (a) The original image (b) The white areas are the suspicious tumor regions before FP reduction. (c) The white area is the result after FP reduction and the solid circle indicates the position of the real tumor.
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Fig. 14 A true-positive case of 3.2 cm infiltrating duct carcinoma. (a) The original image (b) The white areas are the suspicious tumor regions before FP reduction. (c) The white area is the result after FP reduction and the solid circle indicates the position of the real tumor.
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Fig. 15 A true-positive case of 3.5 cm infiltrating duct carcinoma. (a) The original image (b) The white areas are the suspicious tumor regions before FP reduction. (c) The white area is the result after FP reduction and the solid circle indicates the position of the real tumor.
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Fig. 16 A true-positive case of 7.0 cm DCIS. (a) The original image (b) The white area is the suspicious tumor region before FP reduction. (c) The white area is the result after FP reduction and the solid circle indicates the position of the real tumor.
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Fig. 17 A true-positive case of 5.4 cm phyllodes tumor. (a) The original image (b) The white area is the suspicious tumor region before FP reduction. (c) The white area is the result after FP reduction and the solid circle indicates the position of the real tumor.
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(a)
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Fig. 18 A false-negative case of 3.1cm fibroadenomas. (a) The original image (b) The white areas are the suspicious tumor regions before FP reduction. (c) The result after FP reduction and the solid circle indicates the position of the real tumor.
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(a)
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Fig. 19 A false-negative case of 2.0 cm DCIS. (a) The original image (b) The white area is the suspicious tumor region before FP reduction. (c) The result after FP reduction the solid circle indicates the position of the real tumor.
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(a)
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Fig. 20 A false-negative case of 2.5 cm infiltrating duct carcinoma. (a) The original image in A-view (b) The white areas are the suspicious tumor regions before FP reduction from (a). (c) The original image in C-view. (d) The white areas are the suspicious tumor regions before FP reduction from (c).
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(a)
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Fig. 21 A false-positive example. (a) The arrow indicates the rib region in the original image. (b) The white areas are the suspicious tumor region before FP reduction.
(c) The white area is the result after FP reduction and the dot circle indicates the FP. The rib is misclassified as the suspicious tumor region in this case.
4.2 Discussion
US has been shown to be a useful tool for breast tumor detection and has been an useful adjunct to mammography, especially for women with dense breast tissue [10, 11] The ABUS has been an popular screening tool in clinical because its operator-independent, ease for training, time-efficient and better reproducibility for follow-up studies [17]. Due to large amounts of data in the 3-D US images, tumor detection is not an easy task for the physician and the misdiagnosis might be occurred.
Therefore, the CADe systems have been proposed to assist the diagnosis of the
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physician.
In this study, the proposed region-based method can reduce the influences of noises and the strategy that merging the tumor regions after FCM can prevent the tumor regions from connecting other non-tumor regions, improving the segmentation results. In above true-positive examples from Fig. 10 to Fig. 14, the tumors in these examples are surrounded with others darker regions that may connect with the tumor region and causes segmentation distortions. In our proposed method, the suspicious tumor regions generated by FCM are merged within the merging threshold THtumor to segment a real tumor, separating from other non-tumor regions. These segmentation results show that our proposed method can segment the tumor regions well.
However, it is very difficult to choose a perfect merging threshold THtumor to fit all the cases. A few false-negative cases are caused by the merging threshold. For the false-negative case shown in Fig. 18, the segmented tumor region is narrow and the tumor was classified as a non-tumor region in the FP reduction. Since our merging threshold is quite small for this case, the segmented tumor region is just the partial real tumor. Oppositely, the same merging threshold is too large for the case shown in Fig. 19. The position of the tumor in this case is right below the nipple so that the shadows from the nipple affect the segmentation seriously. As a result, with the same merging threshold, the tumor region merges with shadows and causes distortion.
Another false-negative case is shown in Fig. 20 and this malignant tumor is just adjacent to the right anechoic region. Even though the segmentation result seems perform very well in A-view, the tumor region connects with another darker region at the tumor edge. The FP reduction will consider it as a non-tumor region since the boundary between these two regions is ambiguous, as shown in Fig. 20(c)(d).
For a false-positive case shown in Fig. 21, because the rib in this case is in a shape that looks like a tumor, the rib is miss-classified and becomes a FP. In our
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detection results, some parts of ribs are segmented as the suspicious regions that look like tumors; therefore, some further features should be used to classify these tumor-like suspicious regions to be non-tumor.
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