Superpixel and multi-atlas based fusion entropic model for the segmentation of X-ray images

Medical Image Analysis
D C T NguyenF Lavoie

Abstract

X-ray image segmentation is an important and crucial step for three-dimensional (3D) bone reconstruction whose final goal remains to increase effectiveness of computer-aided diagnosis, surgery and treatment plannings. However, this segmentation task is rather challenging, particularly when dealing with complicated human structures in the lower limb such as the patella, talus and pelvis. In this work, we present a multi-atlas fusion framework for the automatic segmentation of these complex bone regions from a single X-ray view. The first originality of the proposed approach lies in the use of a (training) dataset of co-registered/pre-segmented X-ray images of these aforementioned bone regions (or multi-atlas) to estimate a collection of superpixels allowing us to take into account all the nonlinear and local variability of bone regions existing in the training dataset and also to simplify the superpixel map pruning process related to our strategy. The second originality is to introduce a novel label propagation step based on the entropy concept for refining the resulting segmentation map into the most likely internal regions to the final consensus segmentation. In this framework, a leave-one-out cross-validation process was perf...Continue Reading

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