Discrete-time signal processing
Discrete-time signal processing
Automated Chromosome Classification Using Wavelet-Based Band Pattern Descriptors
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Automatic locating the centromere on human chromosome pictures
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Toward a completely automatic neural-network-based human chromosomeanalysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel algorithm for straightening highly curved images of human chromosome
Pattern Recognition Letters
Automatic segmentation and disentangling of chromosomes in Q-band prometaphase images
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Chromosome image recognition with subregion search iteration
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A modular framework for the automatic classification of chromosomes in Q-band images
Computer Methods and Programs in Biomedicine
A chromosome image recognition method based on subregions
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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Karyotyping, a standard method for presenting pictures of the human chromosomes for diagnostic purposes, is a long standing, yet common technique in cytogenetics. Automating the chromosome classification process is the first step in designing an automatic karyotyping system. The main aim in this study was to define a new group of features for better representation and classification of chromosomes. Width, position and the average intensity of the two most eye-catching regions of each chromosome (that we call characteristic bands) are the new proposed features. The concept of a characteristic band is based on the expert cytogeneticists' method in classification of the chromosomes. The length, centromeric index (CI) and an index of overall darkness or brightness of the image (NAGD) were also included in the final nine-dimensional feature vectors describing each chromosome. To automatically find the characteristic bands and calculate the new features, different windows in chromosome's density profile were scored based on their intensity and width. As a feasibility study, our work was focused on classification of chromosomes in group E. Three layer artificial neural networks were employed to classify each chromosome in one of the three possible classes (chromosomes 16, 17 and 18). The best results obtained were accurate classification of up to 98.6% of chromosomes. Particularly a six-dimensional subset of the features showed reproducibly high performances in classification experiments. The results of this feasibility study show that new features inspired from human expert's classification method are potentially capable of improving the accuracy of the karyotyping systems.