Application of Higher Order Image Co-Segmentation in Medical Images

P. Chandrika, Y.Pavan Kumar Reddy


A novel interactive image co-segmentation algorithm using likelihood estimation and higher order energy optimization is proposed for extracting common foreground objects from a group of related images. Our approach introduces the higher order clique’s, energy into the co-segmentation optimization process successfully. A region-based likelihood estimation procedure is first performed to provide the prior knowledge for our higher order energy function. Then, a new co-segmentation energy function using higher order cliques is developed, which can efficiently co-segment the foreground objects with large appearance variations from a group of images in complex scenes. Both the quantitative and qualitative experimental results on representative datasets demonstrate that the accuracy of our co-segmentation results is much higher than the state-of-the-art co-segmentation methods.

The delineation of tumor boundaries in medical images is an essential task for the early detection, diagnosis and follow-up of cancer. However accurate segmentation remains challenging due to presence of noise, inhomogeneity and high appearance variability of malignant tissue. In this paper, we propose an automatic segmentation approach using fully-connected higher-order co-segmentation (HOC) where potentials are computed within a discriminant Grassmannian manifold. First, the framework learns within-class and between-class similarity distributions from a training set of images to discover the optimal manifold discrimination between normal and pathological tissues. Second, the conditional optimization scheme computes non-local pairwise as well as pattern-based higher-order potentials from the manifold subspace to recognize regions with similar libeling’s and incorporate global consistency in the inference process. Our HOC framework is applied in the context of metastatic brain tumor segmentation in CT images. Compared to state of the art methods, our method achieves better performance on a group of 30 intracerebral hemorrhage images and can deal with highly pathological cases.

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