Evaluation of brain tumor using brain MRI with modified-moth-flame algorithm and Kapur’s thresholding: a study

• Published in: Evolutionary Intelligence
• Main Author: Kadry S.; Rajinikanth V.; Raja N.S.M.; Jude Hemanth D.; Hannon N.M.S.; Raj A.N.J.
• Format: Article
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098770706&doi=10.1007%2fs12065-020-00539-w&partnerID=40&md5=9f0b53de2c79b8b80089a423b1528dda

Brain abnormality is a severe illness in humans. An unrecognised and untreated brain illness will lead to a lot of complications despite of gender and age. Brain tumor is one of the severe conditions in humans; begins due to a variety of unavoidable and unpredicted reasons. The clinical level diagnosis of brain tumor is performed with the help of non-invasive imaging procedures, such as Computed-Tomography and Magnetic-Resonance-Imaging. The proposed work implements an image processing procedure to extract the tumor section from the clinical-grade MRI slices recorded with Flair and T2 modalities. This procedure integrates thresholding and segmentation procedures to extract the tumor division from 2D MRI slices with better accuracy. MRI slices with the skull section are considered in this work and the extraction of the tumor is further achieved by implementing the Modified Moth-Flame Optimization algorithm based Kapur’s thresholding and a chosen segmentation technique. Benchmark images of BRAINIX and TCIA-GBM datasets are used in this work for experimental investigation. The outcome establishes the performance values attained with Flair modality images are slightly better compared to T2 modality. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.