Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI. (August 2022)

• Record Type: Journal Article
• Authors: Alpar, Orcan; Dolezal, Rafael; Ryska, Pavel; Krejcar, Ondrej
• Is Part Of: Pattern recognition. Volume 128(2022)
• Publication Date: 2022-08
• Subjects: Nakagami imaging -- Fuzzy c-means -- Lesion segmentation -- MRI -- BraTS; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108675
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We proposed a Nakagami-Fuzzy imaging framework for medical image segmentation This paper would be the first application of Nakagami-Fuzzy protocol to MRI images We enhanced images by Nakagami and segmented lesions by modified fuzzy 2-means We achieved 92.61% dice score for the main clinical experiment we conducted Dice scores are computed as 91.88%/89.25% for BraTS 2012/2020 dataset experiments Abstract: Nakagami distribution and related imaging methods are very efficient in diagnostic ultrasonography for visualization and characterization of tissues for years. Abnormalities in tissues are distinguished from surrounding cells by application of the distribution ruled by the Nakagami m-parameter. The potential of discrimination in ultrasonography enables intelligent segmentation of lesions by other diagnostic tools and the imaging technique is very promising in other areas of medicine, like magnetic resonance imaging (MRI) for brain lesion identification, as presented in this paper. Therefore, we propose a novel Nakagami-Fuzzy imaging framework for intelligent and fully automated suspicious region segmentation from axial FLAIR MRI images exhibiting brain tumor characteristics to satisfy ground truth images with different precision levels. The images from MRI data set are processed by applying Nakagami distribution from pre-Rayleigh to post-Rayleigh for adjusting m-parameter. Amorphous and non-homogenous suspicious regions revealed by Nakagami imaging are segmented using customized Fuzzy 2-means to compare with two types of binary ground truths. The framework we propose is an outstanding example of fuzzy-based expert systems providing an average of 92.61% dice score for the main clinical experiment we conducted using the images and two types of ground truths provided by University of Hospital, Hradec Kralove. We also tested our framework by the BraTS 2012 and BraTS 2020 datasets and achieved an average of 91.88% and 89.25% dice scores respectively, which are competitive among the relevant researches.