Automated human chromosome segmentation and feature extraction: Current trends and prospects [version 1; peer review: 2 approved with reservations]

Automated human chromosome segmentation and feature extraction aim to improve the overall quality of genetic disorder diagnosis by addressing the limitations of tedious manual processes such as expertise dependence, time-inefficiency, observer variability and fatigue errors. Nevertheless, significant differences caused by staining methods, chromosome damage which may occur during imaging, cell and staining debris, inhomogeneity, weak boundaries, morphological variations, premature sister chromatid separation, as well as the presence of overlapping, touching, di-centric and bent chromosomes pose challenges in automated human chromosome segmentation and feature extraction. This review paper extensively discusses how the approaches presented in literature have addressed these challenges, and their strengths and limitations. Human chromosome segmentation algorithms are presented under four broad categories; thresholding, clustering, active contours and convex-concave points-based methods. Chromosome feature extraction methods are discussed under two main categories based on banding-pattern and geometry. In addition, new insights for the improvement of fully automated karyotyping are provided.