TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 19, No 6: December 2021

Brain Tumor Segmentation using Multi-level Otsu Thresholding and Chan-Vese Active Contour Model

Hadi, Heru Pramono (Dian Nuswantoro University)
Faisal, Edi (Dian Nuswantoro University)
Rachmawanto, Eko Hari (Dian Nuswantoro University)



Article Info

Publish Date
01 Dec 2021

Abstract

Research on brain tumor segmentation has been developed, ranging from threshold-based methods to the use of the deep learning algorithm. In this study, we proposed a region-based brain tumor segmentation method, namely the active contour model (ACM). Tumor segmentation was carried out using (Fluid Attenuated Inversion Recovery) FLAIR modality MRI image data obtained from the BRATS 2015 dataset of 86 images. The initial stage of our segmentation method is to find the initial initialization point/area for the ACM algorithm using Multi-Level Otsu Thresholding, with the level used in this study is 3 levels. After the initial initialization area has been obtained, the segmentation process is continued with ACM which explores the tumor area to obtain a full and accurate tumor area result. The results of this study obtained Dice Similarity for our study of 0.7856 with a total time required of 28.080722 seconds, which better than other method that we also compared with ours, 0.75 compared to 0.78 in term of Dice Similarity.

Copyrights © 2021






Journal Info

Abbrev

TELKOMNIKA

Publisher

Subject

Computer Science & IT

Description

Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of ...