A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation

Abstract

In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague boundaries and poor contrast. This novel technique takes into account both the image histogram and the uncertainty information for the computation of multiple thresholds. The benefit of the methodology is that it provides fast and improved segmentation for the complex tumorous images with imprecise gray levels. To further boost the computational speed, the mutation based particle swarm optimization is used that selects the most optimal threshold combination. The accuracy of the proposed segmentation approach has been validated on simulated, real low-grade glioma tumor volumes taken from MICCAI brain tumor segmentation (BRATS) challenge 2012 dataset and the clinical tumor images, so as to corroborate its generality and novelty. The designed technique achieves an average Dice overlap equal to 0.82010, 0.78610 and 0.94170 for three datasets. Further, a comparative analysis has also been made between the eight existing multilevel thresholding implementations so as to show the superiority of the designed technique. In comparison, the results indicate a mean improvement in Dice by an amount equal to 4.00% (p < 0.005), 9.60% (p < 0.005) and 3.58% (p < 0.005), respectively in contrast to the fuzzy tsallis approach.

Magnetic resonance image-based brain tumour segmentation methods: A systematic review

“…After the full-text screening, 223 studies are included for synthesis. Among them, 61 are physics or mathematics-based, 13–74 156 are deep learning-based and six are software-based or semi-automatic 75–80 methods articles.…”

A continuous learning approach to brain tumor segmentation: integrating multi-scale spatial distillation and pseudo-labeling strategies

“…They calculated local texture and abnormality maps and achieved good results in segmenting enhancing tissue, tumor core, and the whole abnormal region in high-grade gliomas. In another study, Kaur et al (12) proposed a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and Tsallis entropy to select tumor regions in MR images with blurred boundaries and poor contrast, further improving the segmentation speed and accuracy. These studies highlight the effectiveness of traditional machine learning techniques in medical image segmentation and lay the foundation for the development of more advanced deep learningbased methods.…”

Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

“…As the generalized form of the Shannon entropy, the Tsallis entropy could describe the correlation described by the nonextensive parameter q between the grayscale value of pixels in images. Image segmentation methods based on the Tsallis entropy considered the interaction between clusters, which is favorable in segmenting images with nonextensive features [28][29][30][31][32]. Among them, a new multilevel thresholding technique based on the Tsallis entropy for colored satellite image has been proposed, achieving segmentation of complex images with small color differences and having the ability to describe the interrelationship between clusters [28].…”

“…Among them, a new multilevel thresholding technique based on the Tsallis entropy for colored satellite image has been proposed, achieving segmentation of complex images with small color differences and having the ability to describe the interrelationship between clusters [28]. Kaur et al have proposed a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and Tsallis entropy for the automatic delineation of tumors in magnetic resonance images with vague boundaries and poor contrast [29]. However, both techniques that applied the fuzzy Tsallis entropy to threshold segmentation still suffer a major limitation, as they can only segment one-dimensional images.…”