Geometric Feature-Based Classification of Segmented Human Chromosomes

Abstract

The chromosomes are the carriers of the geometric information, any alteration in the structure or number of these chromosomes is termed as genetic defect. These alterations cause malfunctioning in the proteins and are cause of the various underlying medical conditions that are hard to cure or detect by normal clinical procedures. In order to detect the underlying causes of these defects, the cells of the humans need to be imaged during the mitosis phase of cell division. During this phase, the chromosomes are the longest and can be easily studied and the alterations in the structure and count of the chromosomes can be analyzed easily. The chromosomes are non-rigid objects, due to which they appear in varied orientations, which makes them hard to be analyzed for the detection of structural defects. In order to detect the genetic abnormalities due to structural defects, the chromosomes need to be in straight orientation. Therefore, in this work, we propose to classify the segmented chromosomes from the metaspread images into straight, bent, touching overlapping or noise, so that the bent, touching, overlapping chromosomes can be preprocessed and straightened and the noisy objects be discarded. The classification has been done using a set of 17 different geometric features. We have proposed a Multilayer Perceptron-based classification approach to classify the chromosomes extracted from metaspread images into five distinct categories considering their orientation. The results of the classification have been analyzed using the segmented objects of the Advance Digital Imaging Research (ADIR) dataset. The proposed technique is capable of classifying the segmented chromosomes with 94.28% accuracy. The performance of the proposed technique has been compared with seven other state-of-the-art classifiers and superior results have been achieved by the proposed method.