Effect of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI

Channu Kokila, K. Ramesh


In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback–Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study.


Expectation maximization (EM), fractal dimension (FD), Kullback–Leibler divergence(KLD), MRImodalities.


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