Automated brain tumor segmentation and classification for MRI analysis system
| Autor: | Norhashimah Mohd Saad, Nor Shahirah Mohd Noor, Muhamad Faizal Yaakub, Abdul Rahim Abdullah, Nur Azmina Zainal, Wira Hidayat Mohd Saad |
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| Rok vydání: | 2019 |
| Předmět: |
Control and Optimization
Jaccard index medicine.diagnostic_test Computer Networks and Communications Computer science business.industry Feature extraction Brain tumor Magnetic resonance imaging Pattern recognition Fluid-attenuated inversion recovery medicine.disease Hardware and Architecture Signal Processing medicine Segmentation Artificial intelligence Electrical and Electronic Engineering business Brain tumor segmentation Information Systems |
| Zdroj: | Indonesian Journal of Electrical Engineering and Computer Science. 15:1337 |
| ISSN: | 2502-4760 2502-4752 |
| DOI: | 10.11591/ijeecs.v15.i3.pp1337-1344 |
| Popis: | This paper proposed a new analysis technique of brain tumor segmentation and classification for Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI). 25 FLAIR MRI images were collected from online database of Multimodal Brain Tumor Segmentation Challenge 2015 (BRaTS’15). The analysis comprised four stages which are preprocessing, segmentation, feature extraction and classification. Fuzzy C-Means (FCM) was proposed for brain tumor segmentation. Mean, median, mode, standard deviation, area and perimeter were calculated and utilized as the features to be fed into a rule-based classifier. The segmentation performances were assessed based on Jaccard, Dice, False Positive and False Negative Rates (FPR and FNR). The results indicate that FCM offered high similarity indices which were 0.74 and 0.83 for Jaccard and Dice indices, respectively. The technique can possibly provide high accuracy and has the potential to detect and classify brain tumor from FLAIR MRI database. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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