Investigation on Feature Selection to Improve Classification of Abdominal Organs in CT Images

This paper presents the preliminary result on feature selection for the purpose of classifying soft tissues of abdominal organs in computer tomography (CT) images. From the images in the dataset, texture features were first extracted, and the most relevant features were identified based on the Information Gain measure. Then a Decision Tree classifier was used to select the optimal subset of features. The initial experiments indicated that, by removing the combinations of the descriptors and distances which have the lowest Information Gain, as much as 83 % of the original features were removed without sacrificing the classification accuracy at all, for the overall dataset or any individual organ, or even improving it significantly for some organs.