Brain Tumor Classification Using MRI Images with K-Nearest Neighbor Method

The accuracy level in diagnosing tumor type through MRI results is required to establish appropriate medical treatment. MRI results can be computationally examined using K-Nearest Neighbor method, a basic science application and classification technique of image processing. Tumor classification system is designed to detect tumor and edema in T1 and T2 images sequences, as well as to label and classify tumor type. Data interpretation of such system derives from Axial section of MRI results only, which is classified into three classes: Astrocytoma, Glioblastoma, and Oligodendroglioma. To detect tumor area, basic image processing technique is employed, comprising of image enhancement, image binarization, morphological image, and watershed. Tumor classification is applied after segmentation process of Shape Extration Feature is undertaken. The results of tumor classification obtained was 89.5 percent, which is able to provide information regarding tumor detection more clearly and specifically.

I. Introduction

Tumors are a commonly used term, but not specific, for neoplasms. The word tumor only refers to the mass. Tumors are general terms that can refer to benign (generally harmless) or malignant (cancer) growth [1]. Cancer is a term used for cell growth (tumors) that are malignant or continue to grow uncontrollably which can threaten the health of the body.