Detection and Validation of Segmentation Techniques for MR Brain Tumor of Glioma Patients
Akshath M J1, H S Sheshadri2
1Akshath M J, Research scholar, ECE, PET-RC, PESCE, Mandya, India.
2H S Sheshadri, Professor, Dept of ECE, PESCE, Mandya, India.
Manuscript received on 29 August 2019. | Revised Manuscript received on 12 September 2019. | Manuscript published on 30 September 2019. | PP: 373-378 | Volume-8 Issue-11, September 2019. | Retrieval Number: K13560981119/2019©BEIESP | DOI: 10.35940/ijitee.K1356.0981119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction of brain tumor becomes difficult with respect to the irregular shape, growth, location and volume of the tumor, thus segmentation is highly required for the proper detection of the tumor. Four sequences of MR images like T1, T2, T1 contrast and fluid attenuation inversion recovery (FLAIR) is collected from the BRATS 2015 dataset for the validation of segmentation techniques. In this paper two segmentation techniques like semi-automated active contour and fullyautomated expectation maximization (EM) are discussed as both are widely used in the field of brain tumor analysis. The synthetic data obtained is skull stripped and noise free reducing the process time for detecting the tumor. The main objective is to extract the region of interest, validate and improve the accuracy, dice coefficient of the synthetic dataset with the ground truth available. Active contour is an iterative process with the initial contour selected manually and EM works on the probability of the intensity values. The result shows some of the images works better with active contour and some with EM. Time taken is less for EM compared to active contour. Accuracy, dice coefficient, sensitivity is better in EM compared to Active contour. Statistical features and textural features extracted from the above techniques plays vital role for the accurate diagnosis of the tumor. In this context segmentation is vital to further classify images into low grade and high grade glioma’s helping radiologists to accurately diagnose the abnormal tissue growth with effective planning of treatment.
Keywords: Active contour, Expectation maximization, Glioma, MR images, Segmentation.
Scope of the Article: