A variable region scalable fitting energy approach for human Metaspread chromosome image segmentation

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The medical images often suffer from intensity inhomogeneity and therefore they are difficult to segment using the conventional image segmentation approaches. In this work an image segmentation approach is being presented, in which the region scalable fitting energy is variable for the regions inside and outside the contours. The energy term is defined along the two sides of the contour, by considering different scale parameters for the foreground and background regions. Two fitting functions are used to approximate the intensities of the foreground and background regions. The proposed method is able to deal with intensity inhomogeneity as a Gaussian kernel function has been used in the energy formulation. The kernel function is used to assign weights to the intensity values during energy formulation. Different scale parameters are used for the kernel function for background and foreground regions. The proposed method proves to be robust as it has the self-regularization capability, partial differential equations have been discretized by approximations and the method does not require computationally expensive re-initialization procedure. The proposed method is capable of efficiently segmenting the human metaspread chromosome images that suffer from intensity inhomogeneity. The chromosomes that have very low contrast with the background or the group of chromosomes that appear to be nearly touching each other are segmented correctly from the metaspread images.