Trainable grey-level models for disentangling overlapping chromosomes
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Cited by (21)
Automatic segmentation of metaphase cells based on global context and variant analysis
2008, Pattern RecognitionChromosome classification based on the band profile similarity along approximate medial axis
2008, Pattern RecognitionCitation Excerpt :For example, chromosomes are often bent, and the colors of the pixels within the same band produced by the staining process are usually nonuniform. As a result, the sampled gray-values are often noisy and contain outliers [6,7]. As stated in Refs. [1,8], the combination of features from both band pattern and shape representation is able to reduce the error rate of karyotyping systems.
Disentangling chromosome overlaps by combining trainable shape models with classification evidence
2002, IEEE Transactions on Signal ProcessingA Web-based Tool for Semi-interactively Karyotyping the Chromosome Images for Analyzing Chromosome Abnormalities
2020, Proceedings - 2020 7th NAFOSTED Conference on Information and Computer Science, NICS 2020Fully-automatic raw G-band chromosome image segmentation
2020, IET Image Processing
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