Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method

被引：11

作者：

Faye, Ibrahima ^{[1
]}

Samir, Brahim Belhaouari ^{[1
]}

Eltoukhy, Mohamed M. M. ^{[2
]}

机构：

[1] Univ Teknol PETRONAS, Fundamental & Appl Sci Dept, Tronoh, Perak, Malaysia

[2] Univ Teknol PETRONAS, Dept Elect & Elect Engn, Tronoh, Perak, Malaysia

来源：

SECOND INTERNATIONAL CONFERENCE ON COMPUTER AND ELECTRICAL ENGINEERING, VOL 2, PROCEEDINGS | 2009年

关键词：

component; Breast cancer; Wavelet tranform; Feature extraction; Digital mammogram; BREAST-CANCER; MICROCALCIFICATIONS; DECOMPOSITION; DIAGNOSIS; SYSTEM;

D O I：

10.1109/ICCEE.2009.39

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper introduces a new method of feature extraction from Wavelet coefficients for classification of digital mammograms. A matrix is constructed by putting Wavelet coefficients of each image of a building set as a row vector. The method consists then on selecting by threshold, the columns which will maximize the Euclidian distances between the different class representatives. The selected columns are then used as features for classification. The method is tested using a set of images provided by the Mammographic Image Analysis Society (MIAS) to classify between normal and abnormal and then between benign and malignant tissues. For both classifications, a high accuracy rate (98%) is achieved.

引用

页码：318 / +

页数：2

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