Automated Prototype Generation for Multi-color Karyotyping

Abstract

This chapter presents an algorithm for automatically generating a prototype from multicolor karyotypes obtained via multi-spectral imaging of human chromosomes. The single representative prototype of the color karyotype that is generated represents the analytical integration of a group of karyotypes obtained via Multicolor Fluorescence In Situ Hybridization (MFISH) method. Multicolor karyotyping is a 24-color MFISH method that allows simultaneous screening of the genome. It allows for the detection of a wide variety of anomalies in human chromosomes, including subtle and complex rearrangements. Although, multicolor karyotyping allows visual detection of gross anomalies, misclassified pixels make manual examination difficult. Additionally, in the absence of prior knowledge of the anomaly, interpretation of the karyotypes can be ambiguous. In this study we have developed an automated method for the generation of a single representative prototype of the color karyotype, which assists the screening of chromosomal aberrations by computational removal of non-physiological anomalies. We hypothesize that generation of a single representative prototype of the color karyotype from multiple karyotypes (k) for a given specimen can highlight all the aberrations, while minimizing misclassified pixels arising from inconsistencies in sample preparation, hybridization and imaging procedures. A three-tier approach is implemented to achieve the generation of the representative color karyotype from a set of multiple (>2) karyotypes. The first step involves the automated extraction of individual chromosomes from each karyotype in the set, followed by chromosome straightening and size normalization. In the second step, the extracted and normalized chromosomes belonging to each of the 24 color classes are automatically assigned to a particular group (1, 2, 3, etc.) based on the ploidy level (monoploid, diploid, triploid, etc.), respectively. For automated group assignment, Bayesian classification is utilized to determine the probability that a particular chromosome belongs to a specific group based on the similarity between the chromosomes within the group. Similarity is evaluated using two distance metrics: (1) two-dimensional (2D) histogram based descriptors, and (2) Eigen space representation based on Principal Component Analysis (PCA). Finally in the third step, we compute the prototype of the color karyotype by generating the representative chromosome for each group in the 24 color classes using pixel-based fusion. This approach allows us to generate the representative prototype color karyotype that reflects all anomalies for a given specimen, while rejecting non-physiological inconsistencies. Furthermore, automation not only reduces the workload, but also allows alleviation of subjectivity by providing a quantitative formulation based on statistical analysis.