NDLI: A New Methodology Based on q-Entropy for Breast Lesion Classification in 3-D Ultrasound Images

Content Provider	IEEE Xplore Digital Library
Author	Rodrigues, P.S. Giraldi, G.A. Provenzano, M. Faria, M.D. Ruey-Feng Chang Suri, J.S.
Copyright Year	2006
Description	Author affiliation: Nat. Lab. for Sci. Comput. (Rodrigues, P.S.; Giraldi, G.A.)
Abstract	Classification of breast lesions is clinically most relevant for breast radiologists and pathologists for early breast cancer detection. This task is not easy due to poor ultrasound resolution and large amount of patient data size. This paper proposes a five step novel and automatic methodology for breast lesion classification in 3-D ultrasound images. The first three steps yield an accurate segmentation of the breast lesions based on the combination of (a) novel non-extensive entropy, (b) morphologic cleaning and (c) accurate region and boundary extraction in level set framework. Segmented lesions then undergo five feature extractions consisting of: area, circularity, protuberance, homogeneity, and acoustic shadow. These breast lesion features are then input to a support vector machine (SVM)-based classifier that classifies the breast lesions between malignant and benign types. SVM utilizes B-spline as a kernel in its framework. Using a data base of 250 breast ultrasound images (100 benign and 150 malignant) and utilizing the cross-validation protocol, we demonstrate system's accuracy, sensitivity, specificity, positive predictive value and negative predictive value as: 95%, 97%, 94%, 92% and 98% respectively in terms of ROC curves and $A_{z}$ areas, better in performance than the current literature offers
Sponsorship	IEEE EMB
Starting Page	1048
Ending Page	1051
File Size	167775
Page Count	4
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.259221
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Ultrasonic imaging Image segmentation Support vector machines Support vector machine classification Breast cancer Cancer detection Entropy Cleaning Data mining
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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