Cellular image segmentation using morphological operators and extraction of features for quantitative measurement

Abstract

To address the issue of blurriness, artifacts, overlapping of cells and uneven dying of histopathology images of breast cancer cells, a computer assisted image analysis and feature extraction methods are proposed in the present paper which include preprocessing, enhancement, segmentation and features extraction. The proposed method is based on the dysplastic features that work on the computation of features for differentiation of benign and malignant cells. Morphological measures are significantly used to analyze these features. The purpose of choosing morphological operators is based on the fact that these operators principally utilize regularities and distribution of the structural features of cells. Analysis of cell morphology is an important factor that aids the complete evaluation of the microscopic cells, examination of the cell behaviour. This also provides the quantitative measure of area, perimeter, intensity, and texture, etc. present in large populations of cells. For the implementation, of proposed method publicly available image data set of 58 images (26 malignant and 32 benign) has been used. It is observed that malignant cells have the considerably greater magnitude for computed features as compared to benign. Significant variation in features values are also found in a case of malignant cells. Apart from this, an efficient approach of segmenting cells, presented in the histopathology images has been shown, that will provide assistance to the pathologist to identify malignant cells. The results reported here can be further used in the classification of cells for benign and malignant categories.