Brain tumor segmentation using neutrosophic expert maximum fuzzy-sure entropy and other approaches. (January 2019)

• Record Type: Journal Article
• Authors: Sert, Eser; Avci, Derya
• Is Part Of: Biomedical signal processing and control. Volume 47(2019)
• Page Start: 276
• Page End: 287
• Publication Date: 2019-01
• Subjects: Fuzzy-sure -- Neutrosophic set -- Support vector machine -- Fuzzy C-means -- Darwinian particle swarm optimization; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.08.025
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Glioblastoma is the most aggressive and most common primary brain tumor in adult. NS is a relatively new field of study that successfully resolves indeterminate situations. EMFSE can automatically detect threshold value at gray level. NS-EMFSE combines NS and EMFSE. Proposed approach realizes automatic tumor edge detection in an MRI image. Abstract: Glioblastoma is the most aggressive and most common primary brain tumor in adult individuals. Magnetic resonance imagery (MRI) is widely used in the brain tumor diagnosis. This study proposes an approach called neutrosophic set – expert maximum fuzzy-sure entropy (NS-EMFSE), which is a successful edge detection approach, by combining two powerful approaches such as neutrosophic set (NS) and expert maximum fuzzy-sure entropy (EMFSE). Thus, a high performance approach is designed for Glioblastoma, which is the most difficult brain tumor segmentation and edge finding process. The proposed NS-EMFSE approach was designed to detect enhancing part of the tumor in brain MRI image. Using maximum fuzzy entropy and fuzzy c-partition methods, EMFSE determines the necessary threshold value to convert images into binary format. NS has been recently proposed as an efficient approach based on neutrosophy theory, and yields remarkably successful results for indeterminate situations. The proposed algorithm was compared to NS with Otsu thresholding (NS-Otsu), support vector machine (SVM), fuzzy c-means (FCM), Darwinian particle swarm optimization (DPSO). SVM, FCM, DPSO algorithms have been so far used for edge detection and segmentation in various fields. In this study, figure of merid (FOM) and jaccard index (JI) tests were carried out to evaluate the performances of these 5 edge detection approaches on 100 MRI images. These tests indicate which approach yields the best performance in enhancing part detection of the tumor in MRI image. Analysis of variance (ANOVA) was performed on FOM and JI data. As a result, the maximum values of FOM and JI results for the NS-EMFSE are 0.984000, and 0.965000, the mean values are 0.933440 and 0.912000, and the minimum values are 0.699000 and 0.671000, respectively. When these statistical results are compared with the statistical results of other 4 approaches, it is understood that the proposed method yields higher FOM and JI results. In addition, other statistical analysis results proved that the proposed NS-EMFSE performed better than other 4 methods.