Breast Cancer is one of the most deadly cancers affecting middle–aged women. Accurate diagnosis and prognosis
are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer
diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides
and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process
involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important.
In this work we compare three powerful segmentation approaches and test their impact on the classification of
breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c–means segmentation
and textural segmentation based on co–occurrence matrix.
Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes
four different classifiers were trained and tested with previously extracted features. The compared classifiers
are Multilayer Perceptron (MLP), Self–Organizing Maps (SOM), Principal Component–based Neural Network
(PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the
best results over the three compared approaches and leads to a good feature extraction with a lowest average
error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron
with an error rate of 3.07% using fuzzy c–means segmentation.