Serial and Parallel Algorithms for the Medial Axis Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward a completely automatic neural-network-based human chromosomeanalysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New features for automatic classification of human chromosomes: A feasibility study
Pattern Recognition Letters
Chromosome classification using dynamic time warping
Pattern Recognition Letters
Computer Methods and Programs in Biomedicine
A novel algorithm for straightening highly curved images of human chromosome
Pattern Recognition Letters
A modular framework for the automatic classification of chromosomes in Q-band images
Computer Methods and Programs in Biomedicine
Segmentation and centromere locating methods applied to fish chromosomes images
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
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Many genetic disorders or possible abnormalities that may occur in the future generations can be predicted through analyzing the shape and morphological characteristics of the chromosomes. Karyotype (a systemized array of the human chromosomes obtained from a single cell either by drawing or by photography using a light microscope [1]) is often used for this purpose. To make a Karyotype it is necessary to identify each one of the 24 chromosomes (22 autosomal and a pair of sex chromosomes) from the microscopic images. The first step to automate this process is then to define the morphological and band pattern based features for each chromosome. An important class of morphological features includes those defined with respect to the location of the chromosome's centromere (part of the chromosome that divides it to the long and short arms). Therefore, localization of centromere is an initial step in designing an automatic karyotyping system. In this paper, an effective algorithm for chromosome image processing and automatic centromere locating is presented. The procedure is based on the calculation and analyzing the vertical and horizontal projection vectors of the binary image of the chromosome. The binary image is obtained using the thresholding of the input image after histogram modification and analyzing. When applied to the real chromosome images supplied by the Cytogenetic Laboratory of the Cancer Institute of the Imam hospital in Tehran, an average accuracy of 96% for Centromere locating is achieved.