Analysis of morphological features of benign and malignant breast cell extracted from fnac microscopic image using the pearsonian system of curves

Abstract

Context: Cytological changes in terms of shape and size of nuclei are some of the common morphometric features to study breast cancer, which can be observed by careful screening of fine needle aspiration cytology (FNAC) images. Aims: This study attempts to categorize a collection of FNAC microscopic images into benign and malignant classes based on family of probability distribution using some morphometric features of cell nuclei. Materials and Methods: For this study, features namely area, perimeter, eccentricity, compactness, and circularity of cell nuclei were extracted from FNAC images of both benign and malignant samples using an image processing technique. All experiments were performed on a generated FNAC image database containing 564 malignant (cancerous) and 693 benign (noncancerous) cell level images. The five-set extracted features were reduced to three-set (area, perimeter, and circularity) based on the mean statistic. Finally, the data were fitted to the generalized Pearsonian system of frequency curve, so that the resulting distribution can be used as a statistical model. Pearsonian system is a family of distributions where kappa (Ò) is the selection criteria computed as functions of the first four central moments. Results and Conclusions: For the benign group, kappa (Ò) corresponding to area, perimeter, and circularity was −0.00004, 0.0000, and 0.04155 and for malignant group it was 1016942, 0.01464, and −0.3213, respectively. Thus, the family of distribution related to these features for the benign and malignant group were different, and therefore, characterization of their probability curve will also be different.

Keywords

• Breast cell
• FNAC
• image processing
• Pearsonian system