title: 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.
authors.affiliation: School of Physics and Engineering, Sun Yat-sen University, Guangzhou 510275, China.
authors.forename: Yachun
authors.initials: Y
authors.lastname: Pang
authors.forename: Li
authors.initials: L
authors.lastname: Li
authors.forename: Wenyong
authors.initials: W
authors.lastname: Hu
authors.forename: Yanxia
authors.initials: Y
authors.lastname: Peng
authors.forename: Lizhi
authors.initials: L
authors.lastname: Liu
authors.forename: Yuanzhi
authors.initials: Y
authors.lastname: Shao
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D016000:Cluster Analysis; D003198:Computer Simulation; D003287:Contrast Media / Q000494:pharmacology*; D055088:Early Detection of Cancer / Q000379:methods; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software
chemical_list: D003287:Contrast Media