Chromosome Feature Extraction and Ideogram-Powered Chromosome Categorization

Abstract

Chromosomal diseases diagnostics is based on identifying chromosomes and detecting abnormalities in them. It is a sophisticated procedure that is performed manually or partially automated, therefore is prone to human error. Automation of such diagnostics is a complex multistage task that presents many challenges. One of such challenges is chromosome feature extraction and classification. Categorizing chromosomes is necessary for determining a diagnosis. This problem has been previously covered by research papers, but there is no complete solution by now. Moreover, the vast majority of proposals rely on Machine Learning (ML) and Neural Networks (NN). Usage of ML-powered solutions may pose a problem due to difficulty of dataset collection - medical data may be diverse, heterogeneous and hard to collect because of its sensitive nature. Given paper presents a proposal of chromosome feature extraction and categorization without NN. It uses Computer Vision (CV) techniques for chromosome feature extraction: medial axis is extracted from a chromosome to identify its direction, and than chromosome bands (colored segments that make up distinct chromosome pattern) are identified along the axis. Having transformed chromosome image into a discrete piece of data, it is passed to a proposed multiple-criteria decision-making algorithm. This algorithm is designed to categorize chromosomes without learning dataset, instead making use of ideogram (schematic reference chromosome) data. The algorithm is focused on processing a chromosome as a set of segments, where each segment can be recognized by a special predicate. A predefined combination of such predicates should allow recognizing chromosome type.