A Novel and Robust Automatic Seed Point Selection Method for Breast Ultrasound Images

被引：0

作者：

Al Mukaddim, Rashid ^{[1
,2
]}

Shan, Juan ^{[3
]}

Kabir, Irteza Enan ^{[1
]}

Ashik, Abdullah Salmon ^{[1
]}

Abid, Rasheed ^{[1
]}

Yan, Zhennan ^{[4
]}

Metaxas, Dimitris N. ^{[4
]}

Garra, Brian S. ^{[5
,6
]}

Islam, Kazi Khairul ^{[1
]}

Alam, S. Kaisar ^{[4
,7
]}

机构：

[1] Islamic Univ Technol, Dept Elect & Elect Engn, Gazipur, Bangladesh

[2] Univ Wisconsin, Dept Elect & Comp Engn, 1415 Johnson Dr, Madison, WI 53706 USA

[3] Pace Univ, Seidenberg Sch CSIS, Comp Sci Dept, New York, NY 10038 USA

[4] Rutgers State Univ, Computat Biomed Imaging & Modeling, Piscataway, NJ USA

[5] US FDA, Silver Spring, MD 20903 USA

[6] Washington DC Vet Affairs Med Ctr, Washington, DC USA

[7] Improlabs Pte Ltd, Singapore, Singapore

来源：

2016 INTERNATIONAL CONFERENCE ON MEDICAL ENGINEERING, HEALTH INFORMATICS AND TECHNOLOGY (MEDITEC) | 2016年

关键词：

Breast-cancer; Computer-aided diagnosis (CAD); Image-segmentation; Quantitative-ultrasound; Ranking-function; Seed-point; Sonography; Tissue-Characterization; ultrasound; COMPUTER-AIDED DIAGNOSIS; SEGMENTATION METHOD;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Accurate segmentation of breast lesions is among the several challenges in the development of a fully automatic Computer-Aided Diagnosis system for solid breast mass classification. Many high level segmentation methods rely heavily on proper initialization and the seed point selection is usually the necessary first step. In this paper, a fully automatic and robust seed point selection method is proposed. The method involves a number of processing steps in both space and frequency domain and endeavors to incorporate the breast anatomical knowledge. Using a database of 498 images, we compared the proposed method with two other state-of-the-art methods; the proposed method outperforms both methods significantly with a success rate of 62.85% vs. 44.97% and 13.05% on seed point select.

引用

页数：5

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