https://scholars.lib.ntu.edu.tw/handle/123456789/322957
標題: | A New Methodology Based on q-Entropy for Breast Lesion Classification in 3-D Ultrasound Images | 作者: | Paulo S. Rodrigues Gilson A. Giraldi Marcia Provenzano Marcelo D. Faria RUEY-FENG CHANG Jasjit S. Suri |
公開日期: | 八月-2006 | 起(迄)頁: | 1048-1051 | 來源出版物: | 2006 International Conference of the IEEE Engineering in Medicine and Biology Society | 摘要: | 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 Az areas, better in performance than the current literature offers. © 2006 IEEE. |
URI: | http://scholars.lib.ntu.edu.tw/handle/123456789/322957 https://www.scopus.com/inward/record.uri?eid=2-s2.0-34047103508&doi=10.1109%2fIEMBS.2006.259221&partnerID=40&md5=41ba8cf1b24ccc2f96237af23fe4a58d |
ISSN: | 05891019 | DOI: | 10.1109/iembs.2006.259221 | SDG/關鍵字: | Biological organs; Entropy; Feature extraction; Radiology; Three dimensional computer graphics; Tumors; Ultrasonics; Boundary extraction; Breast lesion classification; Breast lesions; Morphologic cleaning; Medical imaging; algorithm; article; artificial intelligence; automated pattern recognition; breast tumor; computer assisted diagnosis; echography; echomammography; entropy; evaluation study; female; human; image enhancement; methodology; reproducibility; sensitivity and specificity; three dimensional imaging; Algorithms; Artificial Intelligence; Breast Neoplasms; Entropy; Female; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Ultrasonography, Mammary |
顯示於: | 資訊工程學系 |
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