Shape Similarity Analysis of Regions of Interest in Medical Images

In this work, we introduce a new representation technique of 2D contour shapes and a sequence similarity measure to characterize 2D regions of interest in medical images. First, we define a distance function on contour points in order to map the shape of a given contour to a sequence of real numbers. Thus, the computation of shape similarity is reduced to the matching of the obtained sequences. Since both a query and a target sequence may be noisy, i.e., contain some outlier elements, it is desirable to exclude the outliers in order to obtain a robust matching performance. For the computation of shape similarity, we propose the use of an algorithm which performs elastic matching of two sequences. The contribution of our approach is that, unlike previous works that require images to be warped according to a template image for measuring their similarity, it obviates this need, therefore it can estimate image similarity for any type of medical image in a fast and efficient manner. To demonstrate our method's applicability, we analyzed a brain image dataset consisting of corpus callosum shapes, and we investigated the structural differences between children with chromosome 22q11.2 deletion syndrome and controls. Our findings indicate that our method is quite effective and it can be easily applied on medical diagnosis in all cases of which shape difference is an important clue.