Title: Novel extraction and tumour detection method using histogram study and SVM classification

Authors: Sara Sandabad; Achraf Benba; Yassine Sayd Tahri; Ahmed Hammouch

Addresses: Laboratory LRGE, ENSET, Mohamed V University, Rabat, Morocco ' Laboratory LRGE, ENSET, Mohamed V University, Rabat, Morocco ' Laboratory LRGE, ENSET, Mohamed V University, Rabat, Morocco ' Laboratory LRGE, ENSET, Mohamed V University, Rabat, Morocco

Abstract: In this article we present a new method for detecting and segmenting brain tumour regions weighted brain MRI in T1 (with contrast). This method consists of three main stages: (i) extracting the region of interest (brain) using our EMBE method; (ii) study and histogram analysis of the MRI image to create learning and initialise the classification algorithm will be applied later to retrieve and locate the tumour; (iii) tumour detection and classification using SVM into two classes: tumour class and no-tumour class. Our method will be completed by a characterisation of the tumour area by determining its geometric properties. This work will facilitate later the immense task radiologists to the significant number of MRI images have to deal with daily, and may also be a way for future researchers in order to develop other new methods and develop this research so interesting.

Keywords: MRI scanning; magnetic resonance imaging; support vector machines; feature extraction; tumour detection; histograms; SVM classification; brain tumours; image segmentation; medical images.

DOI: 10.1504/IJSISE.2016.078262

International Journal of Signal and Imaging Systems Engineering, 2016 Vol.9 No.4/5, pp.202 - 208

Received: 10 Apr 2015
Accepted: 08 Feb 2016
Published online: 10 Aug 2016 *

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