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Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
Published on: December 15, 2023
A neutrosophic-entropy based adaptive thresholding segmentation algorithm: A special application in MR images of
1Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Abstract:
Brain MR images are composed of three main regions such as gray matter, white matter and cerebrospinal fluid. Radiologists and medical practitioners make decisions through evaluating the developments in these regions. Study of these MR images suffers from two major issues such as: (a) the boundaries of their gray matter and white matter regions are ambiguous and unclear in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. These two issues make the diagnosis of critical diseases very complex. To solve these issues, this study presented a method of image segmentation based on the neutrosophic set (NS) theory and neutrosophic entropy information (NEI). By nature, the proposed method is adaptive to select the threshold value and is entitled as neutrosophic-entropy based adaptive thresholding segmentation algorithm (NEATSA). In this study, experimental results were provided through the segmentation of Parkinson's disease (PD) MR images. Experimental results, including statistical analyses showed that NEATSA can segment the main regions of MR images very clearly compared to the well-known methods of image segmentation available in literature of pattern recognition and computer vision domains.
Insights
This study introduces a new algorithm, NEATSA, for clearer brain MR image segmentation. It effectively addresses ambiguities in gray and white matter boundaries, improving disease diagnosis.
Area of Science:
- Medical Imaging
- Computer Vision
- Neuroscience
Background:
- Brain MR images contain gray matter, white matter, and cerebrospinal fluid, crucial for medical diagnosis.
- Segmentation challenges include ambiguous boundaries and inhomogeneous structures, complicating disease detection.
- Existing methods struggle with these inherent image complexities.
Purpose of the Study:
- To develop an advanced image segmentation method for brain MR images.
- To overcome limitations of current techniques in delineating gray and white matter regions.
- To enhance the clarity of MR image segmentation for improved diagnostic accuracy.
Main Methods:
- Proposed a novel image segmentation method utilizing neutrosophic set (NS) theory and neutrosophic entropy information (NEI).
- Developed the neutrosophic-entropy based adaptive thresholding segmentation algorithm (NEATSA).
- The algorithm adaptively selects threshold values for segmentation.
Main Results:
- NEATSA successfully segmented key regions in Parkinson's disease (PD) MR images.
- Experimental results demonstrated significantly clearer segmentation compared to established methods.
- Statistical analyses confirmed the superior performance of NEATSA.
Conclusions:
- NEATSA offers a robust solution for segmenting challenging brain MR images.
- The method enhances the visualization of anatomical structures, aiding in disease diagnosis.
- This approach shows promise for improving medical image analysis in pattern recognition and computer vision.
