Polarity-free automatic classification of chromosomes
Computational Statistics & Data Analysis
Machine Learning
Fuzzy Similarity Relations for Chromosome Classification and Identification
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Chromosome Classification Based on Wavelet Neural Network
DICTA '10 Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications
IEEE Transactions on Signal Processing
Toward a completely automatic neural-network-based human chromosomeanalysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The karyotyping step is essential in the genetic diagnosis process, since it allows the genetician to see and interpret patient's chromosomes. Today, this step of karyotyping is a time-cost procedure, especially the part that consists in segmenting and classifying the chromosomes by pairs. This paper presents an image analysis pipeline of banded human chromosomes for automated karyotyping. The proposed pipeline is composed of three different stages: an image segmentation step, a feature extraction procedure and a final pattern classification task. Two different approaches for the final classification stage were studied, and different classifiers were compared. The obtained results shows that Random Forest classifier combined with a two step classification approach can be considered as an efficient and accurate method for karyotyping.