Chan-Vese aided fuzzy C-means approach for whole breast and fibroglandular tissue segmentation: Preliminary application to real-world breast MRI

• Published in: Medical physics (Lancaster) Vol. 52; no. 5; p. 2950
• Main Authors: Qadri, Syed Furqan, Rong, Chao, Ahmad, Mubashir, Li, Jing, Qadri, Salman, Zareen, Syeda Shamaila, Zhuang, Zeyu, Khan, Salabat, Lin, Hongxiang
• Format: Journal Article
• Language: English
• Published: United States 01.05.2025
• Subjects: Algorithms; Breast - diagnostic imaging; Breast Density; Breast Neoplasms - diagnostic imaging; Female; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; medical diagnosis; breast cancer; fuzzy C‐means; segmentation; fibroglandular tissue; medical imaging
• ISSN: 2473-4209; 1522-8541; 0094-2405; 2473-4209
• DOI: 10.1002/mp.17660

Magnetic resonance imaging (MRI) is a highly sensitive modality for diagnosing breast cancer, providing an expanding range of clinical usages that are crucial for the care of women at elevated risk of breast cancer development. Segmentation of the whole breast and fibroglandular tissue (FGT), used to evaluate breast cancer risk, is often manually delineated by radiologists in clinical practice. In this paper, we aim to substitute handcrafted breast density segmentation and categorization. The traditional fuzzy C-means (FCM) enable automatic segmentation but may be susceptible to heterogeneity or sparse FGT distribution in MRI. We develop a new automated technique for the segmentation of whole breast and FGT for the coronal-view MRI. We propose a Chan-Vese (CV) aided FCM segmentation approach for estimating the FGT in the whole breast using fat-suppressed (FS) precontrast T1-weighted breast MRI. We present a methodology pipeline comprising region-of-interest (ROI) extraction, nonparametric non-uniform intensity normalization N4 algorithm-based intensity inhomogeneity correction, skin-layer extraction, and then whole breast and FGT segmentation. Our approach involves the FCM algorithm to assign membership degrees to pixels, distinguishing FGT regions from surrounding adipose tissues by assessing their probability of belonging to specific FGT regions, and subsequently, the region-based active contour CV model leverages these membership degrees to direct contour evolution and enhance segmentation boundaries. The proposed method adeptly tackles common challenges in MRI, including blurred edges, low contrast, and intensity inhomogeneity, with efficiency. We evaluated our approach on the Duke Breast Cancer MRI data (DBCM-data) and achieved good segmentation accuracy in terms of Dice similarity coefficient (DSC), Intersection-over-Union (IoU), and Sensitivity (SEN). Our method demonstrates significant accuracy, achieving a DSC (%) of 93.2 ± 3.3 and 84.1 ± 4.9, IoU (%) of 86.4 ± 3.5 and 73.2 ± 5.1, and SEN 87.3 ± 4.1 and 76.7 ± 4.1 for the segmentations of whole breast and FGT, respectively. Our results demonstrated that the CV-aided FCM approach significantly outperformed the existing methods and resulted in significantly more accurate whole breast and FGT segmentation in MRI data.