Classification of histological images of the endometrium using texture features

To present a texture analysis method in order to achieve texture classification for 240 histological images of the endometrium.

A total of 128 patients with endometrial cancer and 112 subjects with no pathological condition were imaged. For each image 190 texture features were initially extracted, derived from the wavelets, the Gabor filters, and the Law's masks, which were reduced after feature selection in only 4 features.

The images were classified into 2 categories using artificial neural networks, and the reported classification accuracy was 98.1%.

The results showed that there was a strong discrimination between histological images of cancerous and normal tissue of the endometrium, based on the proposed set of texture features.