Abstract
Dicentric chromosomes can form in cells because of exposure to radioactivity. They differ from the regular chromosomes in that they have an extra centromere where the sister chromatids fuse. In this paper we work on chromosome classification into normal and dicentric classes. Segmentation followed by shape boundary extraction and shape based Fourier feature computation was performed. Fourier shape descriptor feature extraction was carried out to arrive at robust shape descriptors that have desirable properties of compactness and invariance to certain shape transformations. Support Vector Machine algorithm was used for the subsequent two-class image classification.
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Prakash, S., Chaudhury, N.K. (2017). Dicentric Chromosome Image Classification Using Fourier Domain Based Shape Descriptors and Support Vector Machine. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_20
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DOI: https://doi.org/10.1007/978-981-10-2107-7_20
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