Detection of corpus callosum malformations in pediatric population using the discriminative direction in multiple kernel learning.

Abstract

In this paper we propose a Multiple Kernel Learning (MKL) classifier to detect malformations of the Corpus Callosum (CC) and apply it in a pediatric population. Furthermore, we extend the concept of discriminative direction to the linear MKL methods, implementing it in a single subject analysis framework. The CC is characterized using different measures derived from Magnetic Resonance Imaging (MRI) data and the MKL approach is used to efficiently combine them. The discriminative direction analysis highlights those features that lead the classification for each given subject. In the case of a CC with malformation this means highlighting the abnormal characteristics of the CC that guide the diagnosis. Experiments show that the method correctly identifies the malformative aspects of the CC. Moreover, it is able to identify dishomogeneus, localized or widespread abnormalities among the different features. The proposed method is therefore suitable for supporting neuroradiologists in the decision-making process, providing them not only with a suggested diagnosis, but also with a description of the pathology.