Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images

Abstract

Lymphomas are neoplasms that originate in the lymphatic system and represent one of the most common types of cancer found in the World population. The feature analysis may contribute toward results of higher relevance in the classification of the lesions. Feature extraction methods are employed to obtain data that can indicate lymphoma incidence. In this work, we investigated the multiscale and multidimensional fractal geometry with colour channels and colour models for classification of lymphoma tissue images. The fractal features were extracted from the RGB and LAB models and colour channels. The fractal features were concatenated to form the feature vector. Finally, we employed the Hermite polynomial classifier in order to evaluate the performance of the proposed approach. The colour channels obtained of histological images achieved higher accuracy values, the obtained rates were between 94% and 97%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the lesion in lymphoma images.