Abstract
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.
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Goienetxea, I., Barandiaran, I., Jauquicoa, C., Maclair, G., Graña, M. (2012). Image Analysis Pipeline for Automatic Karyotyping. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28931-6_38
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DOI: https://doi.org/10.1007/978-3-642-28931-6_38
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