Classification of brain tumors by mining MRS spectrums using LabVIEW metabolite peak height scanning method

In this paper, we deal with the problem of classification of brain tumors as normal, benign or malignant using information from magnetic resonance spectroscopy (MRS) image to assist in clinical diagnosis. This paper proposes a novel approach to extract metabolite values represented in a graphical form in MR spectroscopy image. Metabolites like N-acetyl aspartate (NAA), Choline (Cho) and Creatine (Cr) are used to detect the brain tumor. The metabolite ratios NAA/Cho, Cho/Cr and NAA/Cr play most important role in deciding the tumor type. The proposed approach consists of several steps including preprocessing, metabolite peak height scanning and classification. Proposed system stores the metabolite values in dataset instead of storing MRS images; so reduces the image processing tasks and memory requirements. Further these metabolite values and ratios are fed to a k-NN classifier. Experimental results demonstrate the effectiveness of the proposed approach in classifying the brain tumors.