Article citationsMore>>

Dahab, D.A., Ghoniemy, S.S.A. and Selim, G.M. (2012) Automated Brain Tumor Detection and Identification Using Image Processing and Probabilistic Neural Network Techniques. International Journal of Image Processing and Visual Communication, 1, 1-8.

has been cited by the following article:

• TITLE: Grading of Brain Tumors by Mining MRS Spectrums Using LabVIEW —Metabolite Peak Height Scanning Method

  AUTHORS: Jayalaxmi S. Gonal, Vinayadatt V. Kohir

  KEYWORDS: MR Spectrum, Metabolite Peak Height, Graph Scanning, Vision Assistant, LabVIEW, BPN Classifier

  JOURNAL NAME: Open Journal of Medical Imaging, Vol.7 No.1, March 3, 2017

  ABSTRACT: In this paper, we attempt to resolve the problem of grading of brain tumors as grade 2, grade 3, grade 4, 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) along with the metabolite ratios NAA/CHO and presence/absence of LACTATE peak play the 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 BPN classifier. Experimental results demonstrate the effectiveness of the proposed approach in classifying the brain tumors.