State-of-the Art Optimal Multilevel Thresholding Methods for Brain MR Image Analysis

Abstract

The brain MR image analysis is a primary non-invasive component to detect any abnormality in the brain. It is a very important application in the field of medical image processing. For analysing brain MR images, there is a strong need to develop efficient image segmentation methods. Over the years, many image segmentation techniques have been suggested and their real life applications have also been studied. Implementation of these segmentation techniques in biomedical engineering is a major breakthrough. Intensive research works have been carried out explicitly on the analysis of human brain images and their subsequent detection of lesion cells using different segmentation methods. One of the easiest and most generally used method of segmentation is multilevel thresholding due to its precision and robustness against the other methods. To solve the problem of computational complexity for increasing threshold levels, various optimization algorithms are used for optimal multilevel thresholding. In this paper, an attempt is made to present a comprehensive review on the recent advancements in the area of brain MR image segmentation using optimal multilevel thresholding. This review is unique of its kind due to its exclusive emphasis on segmentation of brain MR image using thresholding technique only, which may not be present in the existing literature reviews. Different validation measures used for the multilevel image thresholding are discussed. A detailed comparison of the results obtained over the years is done. The merits and demerits of the methods are highlighted. This compilation aims to aid and encourage researchers to further explore the research in this direction.

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