Document Detail


Computerized segmentation and characterization of breast lesions in dynamic contrast-enhanced MR images using fuzzy c-means clustering and snake algorithm.
MedLine Citation:
PMID:  22952558     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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:
Yachun Pang; Li Li; Wenyong Hu; Yanxia Peng; Lizhi Liu; Yuanzhi Shao
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-08-21
Journal Detail:
Title:  Computational and mathematical methods in medicine     Volume:  2012     ISSN:  1748-6718     ISO Abbreviation:  Comput Math Methods Med     Publication Date:  2012  
Date Detail:
Created Date:  2012-09-06     Completed Date:  2013-01-10     Revised Date:  2013-07-12    
Medline Journal Info:
Nlm Unique ID:  101277751     Medline TA:  Comput Math Methods Med     Country:  United States    
Other Details:
Languages:  eng     Pagination:  634907     Citation Subset:  IM    
Affiliation:
School of Physics and Engineering, Sun Yat-sen University, Guangzhou 510275, China.
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MeSH Terms
Descriptor/Qualifier:
Adult
Aged
Algorithms
Area Under Curve
Breast Neoplasms / diagnosis*,  pathology*
Cluster Analysis
Computer Simulation
Contrast Media / pharmacology*
Early Detection of Cancer / methods
Female
Fuzzy Logic
Humans
Image Interpretation, Computer-Assisted / methods
Magnetic Resonance Imaging / methods*
Middle Aged
Pattern Recognition, Automated / methods
Software
Chemical
Reg. No./Substance:
0/Contrast Media
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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