Automated Prototype Generation for Multi-color Karyotyping

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 X. Wu · S. Shah · F. Merchant ( ) Department of Computer Science, University of Houston, Houston, TX 77204, USA e-mail: [email protected] F. Merchant Department of Engineering Technology, University of Houston, Houston, TX 77204, USA M.E. Celebi, G. Schaefer (eds.), Color Medical Image Analysis, Lecture Notes in Computational Vision and Biomechanics 6, DOI 10.1007/978-94-007-5389-1_8, © Springer Science+Business Media Dordrecht 2013 145