Classification of microscopic images of breast tissue

Lucia Ballerini; Lennart Franzen

doi:10.1117/12.535670

12 May 2004 Classification of microscopic images of breast tissue

Lucia Ballerini, Lennart Franzen

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.535670
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Breast cancer is the most common form of cancer among women. The diagnosis is usually performed by the pathologist, that subjectively evaluates tissue samples. The aim of our research is to develop techniques for the automatic classification of cancerous tissue, by analyzing histological samples of intact tissue taken with a biopsy. In our study, we considered 200 images presenting four different conditions: normal tissue, fibroadenosis, ductal cancer and lobular cancer. Methods to extract features have been investigated and described. One method is based on granulometries, which are size-shape descriptors widely used in mathematical morphology. Applications of granulometries lead to distribution functions whose moments are used as features. A second method is based on fractal geometry, that seems very suitable to quantify biological structures. The fractal dimension of binary images has been computed using the euclidean distance mapping. Image classification has then been performed using the extracted features as input of a back-propagation neural network. A new method that combines genetic algorithms and morphological filters has been also investigated. In this case, the classification is based on a correlation measure. Very encouraging results have been obtained with pilot experiments using a small subset of images as training set. Experimental results indicate the effectiveness of the proposed methods. Cancerous tissue was correctly classified in 92.5% of the cases.

Citation Download Citation

Lucia Ballerini and Lennart Franzen "Classification of microscopic images of breast tissue", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.535670

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