Normal mammogram classification based on regional analysis

被引：0
|
作者：
Sun, YJ [1 ]
Babbs, CF [1 ]
Delp, EJ [1 ]
机构：
[1] Purdue Univ, Sch Elect & Comp Engn, Video & Image Proc Lab, W Lafayette, IN 47907 USA
来源：
2002 45TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL II, CONFERENCE PROCEEDINGS | 2002年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The majority of screening mammograms are normal. It will be beneficial if a detection system is designed to help radiologists readily identify normal regions of mammograms. In this paper, we will present a binary tree classifier based on the use of global features extracted from different levels of a 2-D Quincunx wavelet decomposition of normal and abnormal regional images. This classifier is then used to classify whether an entire whole-field mammogram is normal. This approach is fundamentally different from other approaches that identify a particular abnormality in that is independent of the particular type of abnormality.
引用
收藏
页码：375 / 378
页数：4
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