Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm

Abstract

This paper presents a novel two-step approach that incorporates fuzzy c-means (FCMs) clustering and gradient vector flow (GVF) snake algorithm for lesions contour segmentation on breast magnetic resonance imaging (BMRI). Manual delineation of the lesions by expert MR radiologists was taken as a reference standard in evaluating the computerized segmentation approach. The proposed algorithm was also compared with the FCMs clustering based method. With a database of 60 mass-like lesions (22 benign and 38 malignant cases), the proposed method demonstrated sufficiently good segmentation performance. The morphological and texture features were extracted and used to classify the benign and malignant lesions based on the proposed computerized segmentation contour and radiologists' delineation, respectively. Features extracted by the computerized characterization method were employed to differentiate the lesions with an area under the receiver-operating characteristic curve (AUC) of 0.968, in comparison with an AUC of 0.914 based on the features extracted from radiologists' delineation. The proposed method in current study can assist radiologists to delineate and characterize BMRI lesion, such as quantifying morphological and texture features and improving the objectivity and efficiency of BMRI interpretation with a certain clinical value.

Enhancement of Magnetic Resonance Images Using Soft Computing Based Segmentation

“…This method is not applied on large and natural data to evaluate further results. Another method proposed by M. Masroor [7] for the brain MR image segmentation by the combination of k-means algorithm and Perona Malik Anisotropic filter. This method used Anisotropic diffusion filter and performed some morphological operations for the image enhancement.…”

“…Clustering: K-Means clustering [7], [11], [12] also known as hard clustering. It is an unsupervised clustering method successfully applied in many fields like image segmentation, classification, pattern recognition, astronomy and classifier designs etc.…”

Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm

“…In DCE-MR imaging, differences can be perceived on the time axis also. Various DCE-MRI breast lesion segmentation methods have been developed to allow for rapid and more accurate image interpretation and diagnosis [14][15][16][17][18][19][20][21][22][23]. Among many MRI segmentation methods, artificial intelligence techniques draw more attention from researchers for using it for breast DCE-MRI segmentation.…”

Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast

“…Multiple methods for (semi)automatic tumor segmentation based on MRI images have been described for different tumors, including breast [2][3][4][5][6], prostate [7,8], brain [9][10][11], and head and neck [12] tumors. A variety of segmentation algorithms were used in these studies, including volume growing, threshold-based methods,…”