Comput Math Methods Med - Computerized segmentation and characterization of breast lesions in dynamic contrast-enhanced MR images using fuzzy c-means clustering and snake algorithm.

Tópicos

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Resumo

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.

Resumo Limpo

paper present novel twostep approach incorpor fuzzi cmean fcms cluster gradient vector flow gvf snake algorithm lesion contour segment breast magnet reson imag bmri manual delin lesion expert mr radiologist taken refer standard evalu computer segment approach propos algorithm also compar fcms cluster base method databas masslik lesion benign malign case propos method demonstr suffici good segment perform morpholog textur featur extract use classifi benign malign lesion base propos computer segment contour radiologist delin respect featur extract computer character method employ differenti lesion area receiveroper characterist curv auc comparison auc base featur extract radiologist delin propos method current studi can assist radiologist delin character bmri lesion quantifi morpholog textur featur improv object effici bmri interpret certain clinic valu

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