Segmentation methods in image analysis using medial representations

Traditionally, shape analysis is mostly used in representation and statistical analysis of single objects, and the goal is to discriminate between two populations of objects. I will focus on the method present in paper <em>Multi-object Analysis of Volume, Pose, and Shape using Statistical Discrimination</em>. This paper presents a new methodology of discriminant analysis for multiple objects. The discriminant method is called distance weighted discriminant (DWD). It is a method similar to SVM, but it is useful when presenting new and untrained samples. The advantage is its generalization ability in high dimension and low sample sizes settings. Essentially, distance weighted discrimination is a process of finding the best hyperplane that separate two populations. It is a method similar to SVM that uses an optimization method to find the maximum of the distance between the hyperplane and data points. This paper uses data from clinical pediatric autism study that includes a total of 70 samples. Then, using m-rep to discuss different features like shape, volume and pose and to find out which is more significant in discriminating populations.

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