Overall performance analysis

Figure 10 shows the original MR images and the detection results of the proposed scheme. In Figures 10(a), 10(c), and 10(e), the true tumor regions are indicated with red lines drawn by physicians. The right column of Fig. 10 shows the detection result (white areas) of the proposed scheme. We observe that the proposed scheme can detect tumor regions correctly, as shown in Fig. 10(b), Fig. 10(d), and Fig. 10(f). Compared with Fig. 9, some candidate regions are removed by the following intra-, texture-, and inter-slice analyses.

Moreover, as shown in Fig. 10, these tumors among three cases can be detected though their sizes and locations are different. The results show that combining the intra-slice, texture, and inter-slice analyses improves the performance of the proposed scheme. The main reason is that after ROI selection, the texture information of these candidates is analyzed by using a neural network and their continuity is also evaluated. The different kinds of features combined by using a fuzzy classifier are used to identify tumors. However, there are still misclassified regions shown in Fig. 10 (d). Those regions result from the presence of blood vessels, ducts, or noise.

A receiver operating characteristic (ROC) graph is a common technique for visualizing and evaluating the performance of a classifier [10],[17]. Basically, a classifier’s performance is considered good if the trend of its ROC curve is toward the upper left-hand corner of the graph, i.e., the true positive (TP) rate is higher and the false positive (FP) rate is lower [33], [34],[36-38]. A higher TP rate means tumors can be correctly detected, whereas a lower FP rate which is equivalent to a higher true negative (TN) rate represents normal cases can be correctly identified. In this scenario, the area under the ROC curve should be close to 1.

Here we randomly select a part of MRI slices for training and the other for testing to evaluate the proposed scheme 250 times. For example, we randomly choose three MRI cases

for training and the other for testing. In addition, we also change the threshold in the fuzzy classification to measure TPs and FPs for generating the ROC curve. Figure 11 shows that the ROC curve of our proposed fuzzy classifier approaches the upper left-hand corner of the graph, and the area under the curve is 0.91. Therefore, the results demonstrate that the proposed fuzzy classifier is effective in tumor detection for MR images.

(a) (b)

(c) (d)

(e) (f)

Fig. 10. The detection result for three cases: (a)(c)(e) original MR images (left) and (b)(d)(f) detection results (right)

Here the sensitivity (also called recall), the positive predictive value (PPV) (also called precision) and specificity (i.e., TN) rates are also measured to evaluate the performance of the proposed scheme [17]. Sensitivity is a metric of completeness, whereas PPV and specificity are measures of accuracy for tumors and non-tumors, respectively. The higher the sensitivity, PPV and specificity rates, the better will be the performance of the proposed tumor detector.

For the test samples, the sensitivity rate of the proposed scheme is 100%, but the PPV and specificity rates are 79.4% and 82.1%, respectively. The results show that our proposed scheme can distinguish true tumors from normal regions effectively, but there are still some false alarms. Although the PPV rate is only 79.4%, physicians consider that the number of false alarms is within an acceptable range.

Fig. 11. The ROC curve of the proposed fuzzy classifier

VI. CONCLUSION

In this paper, we have proposed a feature-based scheme that comprises preprocessing, feature extraction, and a fuzzy classifier for suspicious region detection and identification. In the preprocessing phase, we first perform automatic ROI extraction to find out ROIs and then

coarsely determine suspicious candidate regions via candidate screening which is composed of a morphological operator, an adaptive thresholding and ellipse-based approximation. To identifysuspicious regions correctly for breast MR imaging, some features are extracted based on intra-slice, texture, and inter-slice analyses. In intra-slice analysis, the intensity and size information is utilized to find the candidates tumor regions. To localize a suspicious region accurately for further inspection, we propose a region growing algorithm based on the intensity and distance information. Some texture cues are extracted from different domains and merged to form a combined texture feature by using a supervised neural network during texture analysis. In inter-slice analysis, the continuity and size consistency of a suspicious region across slices is exploited to remove noise resulting from other tissues. After feature extraction, we use a fuzzy classifier to integrate the four kinds of features, which are then used to detect suspicious regions in the proposed scheme.

Several MRI cases are utilized to evaluate the performance of the proposed scheme.

The weighted Hausdorff distance is 5.46 pixels for 100 MRI slices. The result shows that the proposed ROI extraction can effectively remove some part of the thoracic cavity and reduce the number of false alarms substantially. In addition, the sensitivity and specificity rates of the proposed scheme are 100% and 82.1%, respectively. The results demonstrate that our scheme can be effective in detecting tumor regions from MR images.

In future, we may extract more features from MR images for suspicious region detection. In addition, we only analyze the MRI sequence captured 7 minutes after injecting the contrast. We may analyze other MRI sequences and then fuse those results for improving the performance of the proposed scheme.

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