Mass Candidate Detection and Segmentation in Digitized Mammograms

被引：5

作者：

Mohamed, S. S. ^{[1
]}

Behiels, G. ^{[1
]}

Dewaele, P. ^{[1
]}

机构：

[1] Agfa Healthcare, Waterloo, ON, Canada

来源：

IEEE TIC-STH 09: 2009 IEEE TORONTO INTERNATIONAL CONFERENCE: SCIENCE AND TECHNOLOGY FOR HUMANITY | 2009年

关键词：

Mammo; cancer; Fat-model; segmentation; Gabor; COMPUTER-AIDED DIAGNOSIS; BREAST MASSES; CLASSIFICATION; FILTERS; CANCER;

D O I：

10.1109/TIC-STH.2009.5444438

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper introduces a system for identifying candidate masses in digitized mammograms. Mass identification is a basic component in Computer-Aided Detection (CAD) systems for mammograms. The proposed algorithm is a cascaded filtering process that consists of several stages: First, a new breast fat model is introduced and the fat content in the image is estimated and removed from the image to obtain a fatless image at a standard resolution. Next, a Gabor filter is specially designed and tailored to fit the mass detection problem and then applied to the fatless image. Finally, the resulting image is segmented to obtain iso-contours. Candidate regions are then identified by contour processing and selection. The proposed algorithm obtained 100% sensitivity with 3.4 false positives per image

引用

页码：557 / 562

页数：6

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