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Cellular Oncology
Volume 27, Issue 4, Pages 237-244
http://dx.doi.org/10.1155/2005/526083

Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas

Hae-Gil Hwang,1 Hyun-Ju Choi,1 Byeong-Il Lee,2 Hye-Kyoung Yoon,3 Sang-Hee Nam,4 and Heung-Kook Choi1

1School of Computer Engineering, Inje University, Republic of Korea
2Department of Nuclear Medicine, Chonnam National University, Republic of Korea
3Department of Pathology, Inje University, Republic of Korea
4Medical Imaging Research Center, Inje University, Republic of Korea

Copyright © 2005 Hindawi Publishing Corporation and the authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (CA). To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant) analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each), respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function.