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Computer-Aided Detection of Breast Lesions in Dce-Mri Using Region Growing Based on Fuzzy C-Means Clustering and Vesselness Filter Publisher



B Shokouhi S1 ; Fooladivanda A1 ; Ahmadinejad N2
Authors

Source: Eurasip Journal on Advances in Signal Processing Published:2017


Abstract

A computer-aided detection (CAD) system is introduced in this paper for detection of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The proposed CAD system firstly compensates motion artifacts and segments the breast region. Then, the potential lesion voxels are detected and used as the initial seed points for the seeded region-growing algorithm. A new and robust region-growing algorithm incorporating with Fuzzy C-means (FCM) clustering and vesselness filter is proposed to segment any potential lesion regions. Subsequently, the false positive detections are reduced by applying a discrimination step. This is based on 3D morphological characteristics of the potential lesion regions and kinetic features which are fed to the support vector machine (SVM) classifier. The performance of the proposed CAD system is evaluated using the free-response operating characteristic (FROC) curve. We introduce our collected dataset that includes 76 DCE-MRI studies, 63 malignant and 107 benign lesions. The prepared dataset has been used to verify the accuracy of the proposed CAD system. At 5.29 false positives per case, the CAD system accurately detects 94% of the breast lesions. © 2017, The Author(s).
1. Breast-Region Segmentation in Mri Using Chest Region Atlas and Svm, Turkish Journal of Electrical Engineering and Computer Sciences (2017)
2. Localized-Atlas-Based Segmentation of Breast Mri in a Decision-Making Framework, Australasian Physical and Engineering Sciences in Medicine (2017)
3. Spatiotemporal Features of Dce-Mri for Breast Cancer Diagnosis, Computer Methods and Programs in Biomedicine (2018)
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