Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter

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.