Identifying Centromere Position of Human Chromosome Images using Contour and Shape based Analysis

•Centromere identification by concave function and weighted shortest path calculation.•SVM for classifying centromere and non-centromere region in chromosomes.•Chromosome abnormality detection based on centromere position.•Comparative analysis of MAT, Projection vector and concavity function. The most significant information of the shape of any image/object is concentrated in curvature regions along the contour and objects boundaries rather than uniformly distributed contour. The points belonging to greater magnitude of curvature gives more meaningful information about the shape of an object. The sign of the curvature can be positive (convex) and negative (concave), the negative curvature information is most significant for segmentation. The contour and region based geometry gives a better visual representation of the shape of an object and helps in identifying the centromere position in chromosomes. Centromere of a chromosome is the constriction point which divides the chromosome into two sections or arms. The two arms are p arm (short arm) and q arm (long arm). The size of the arms are calculated with respect to the position of the centromere. The centromere is identified using boundary concavity method which helps in detecting the dominant points (centromere points) in chromosomes. The method uses the concave function and weighted shortest path calculation for centromere detection. SVM classifier is used for improving the accuracy in detecting the centromere of the chromosomes. As the classifier is binary classifier, it helps in recognizing the centromere and non-centromere regions in chromosomes. Comparative analysis is performed with two other methods (i) Medial Axis Transform (MAT) and (ii) Projection Vector. Boundary concavity proves to be efficient for straight, bent and severely bent chromosomes.