Extracted haralick's texture features and morphological parameters from segmented multispectrale texture bio-images for classification of colon cancer cells

The automatic recognition and classification of biomedical objects can enhance work efficiency while identifying new inter-relationships among biological features. In this paper two features types, Haralick's features based GLCM are applied for classification of cancer cell of textured images and morphological parameters based of cells detection. The objective in our work is the selection of the most discriminating parameters for cancer cells classification. In this work, a new approach aiming to detect and classify colon cancer cells is presented. Our detection approach was derived from the "Snake" method but using a progressive division of the dimensions of the image to achieve faster segmentation. Classification of three cell types was based on nine morphological parameters and five Haralick's features on probabilistic neural network. Three morphological parameters and three Haralick's features were used to assess the efficiency classifications models, including Benign Hyperplasia (BH), Intraepithelial Neoplasia (IN) that is a precursor state for cancer, and Carcinoma (Ca) that corresponds to abnormal tissue proliferation (cancer). Results showed that segmentation of microscopic images using this technique was of higher efficiency than the conventional Snake method. The time consumed during segmentation was decreased to more than 50%. The efficiency of this method resides in its ability to segment Ca type cells that was difficult through other segmentation procedures. Among the nine parameters morphology and five Haralick's features used to classify cells, only three morphologic parameters (Area, Xor convex and Solidity) and three Haralick's features (Correlation, Entropy and Contrast) were found to be effective to discriminate between the three types of cells. In addition, classification of unknown cells was possible using the morphology method. However, some IN cells were wrongly classified as BH cells due to their shapes that were similar to those of BH cells. On the other side, the classification based on three parameters (Correlation, Entropy and Contrast) were found to be effective to discriminate between the three types of cells without wrong. The results obtained using several images show the efficacy of our proposed method.