Ill-conditioning in neural network training problems
SIAM Journal on Scientific Computing
Genetic algorithm for applying constraints in chromosome classification
Pattern Recognition Letters - Special issue on genetic algorithms
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Polarity-free automatic classification of chromosomes
Computational Statistics & Data Analysis
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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
Automated classification of metaphase chromosomes: Optimization of an adaptive computerized scheme
Journal of Biomedical Informatics
Automatic segmentation and disentangling of chromosomes in Q-band prometaphase images
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Automatic locating the centromere on human chromosome pictures
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
IEEE Transactions on Signal Processing
A review of thresholding strategies applied to human chromosome segmentation
Computer Methods and Programs in Biomedicine
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Abstract: The manual analysis of the karyogram is a complex and time-consuming operation, as it requires meticulous attention to details and well-trained personnel. Routine Q-band laboratory images show chromosomes that are randomly rotated, blurred or corrupted by overlapping and dye stains. We address here the problem of robust automatic classification, which is still an open issue. The proposed method starts with an improved estimation of the chromosome medial axis, along which an established set of features is then extracted. The following novel polarization stage estimates the chromosome orientation and makes this feature set independent on the reading direction along the axis. Feature rescaling and normalizing techniques take full advantage of the results of the polarization step, reducing the intra-class and increasing the inter-class variances. After a standard neural network based classification, a novel class reassignment algorithm is employed to maximize the probability of correct classification, by exploiting the constrained composition of the human karyotype. An average 94% of correct classification was achieved by the proposed method on 5474 chromosomes, whose images were acquired during laboratory routine and comprise karyotypes belonging to slightly different prometaphase stages. In order to provide the scientific community with a public dataset, all the data we used are publicly available for download.