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Genetic disorders and malignancies due to the chromosomal abnormalities are being researched in cytogenetics till date. G-banded metaphase images are analyzed, chromosomes pairs are identified and arranged into 23 classes as per ISCN ideogram features through karyotyping. This enables the cytogenetic experts to visualize and detect the chromosomal aberrations at ease. Although, the design of a fully automated karyotyping system is difficult, it eliminates the barriers of manual karyotyping. Here, we propose preprocessing techniques for G-banded metaphase images for the design of automated karyotyping system. Our method starts with a decision tree classifier that classifies the input images into analyzable and un-analyzable. Analyzable metaphase images are denoised by median filter and bilateral filter. Denoised images are enhanced using Iterative contrast limited adaptive histogram equalization and are segmented based on contour. Our method ends with an ANN classifier that classifies the segmented images into single straight, bended, touching and overlapped based on the top ten Chi square selected GLCM geometrical features.

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