NDLI: Classification of breast tissue images based on wavelet transform using discriminant analysis, neural network and SVM

Content Provider	IEEE Xplore Digital Library
Author	Hae-Gil Hwang Hyun-Ju Choi Byoung-Doo Kang Hye-Kyoung Yoon Hee-Cheol Kim Sang-Kyoon Kim Heung-Kook Choi
Copyright Year	2005
Description	Author affiliation: Sch. of Comput. Eng., Inje Univ., Gimhae, South Korea (Hae-Gil Hwang; Hyun-Ju Choi; Byoung-Doo Kang)
Abstract	In this paper, we described breast tissue image analyses using texture features from Haar wavelet transformed images to classify breast lesion of ductal organ Benign, DCIS and CA. The approach for creating a classifier is composed of 2 steps: feature extraction and classification. Therefore, in the feature extraction step, we extracted texture features from wavelet transformed images with 10/spl times/ magnification. In the classification step, we created three classifiers from each image of extracted features using statistical discriminant analysis, neural networks (back-propagation algorithm) and SVM (support vector machines). In this study, we conclude that the best classifier in histological sections of breast tissue in the texture features from second-level wavelet transformed images used in discriminant function.
Sponsorship	IEEE Inje Univ. Busan Convention Bureau
Starting Page	345
Ending Page	349
File Size	779496
Page Count	5
File Format	PDF
ISBN	0780389409
DOI	10.1109/HEALTH.2005.1500478
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-23
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Breast tissue Wavelet analysis Wavelet transforms Image analysis Neural networks Support vector machines Support vector machine classification Feature extraction Image texture analysis Lesions
Content Type	Text
Resource Type	Article

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