Maximum 3D Tsallis entropy based multilevel thresholding of brain MR image using attacking Manta Ray foraging optimization

2021 ◽  
Vol 103 ◽  
pp. 104293
Author(s):  
Bibekananda Jena ◽  
Manoj Kumar Naik ◽  
Rutuparna Panda ◽  
Ajith Abraham
Keyword(s):  
Tsallis Entropy ◽  
Multilevel Thresholding ◽  
Mr Image ◽  
Manta Ray

Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion

2019 ◽  
Vol 10 (3) ◽  
pp. 91-106
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Anisotropic Diffusion ◽  
Particle Swarm ◽  
Structural Similarity ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Mr Image ◽  
Swarm Optimization ◽  
Mr Image Segmentation

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm

2013 ◽  
Vol 11 ◽  
pp. 16-30 ◽  
Author(s):  
Sanjay Agrawal ◽  
Rutuparna Panda ◽  
Sudipta Bhuyan ◽  
B.K. Panigrahi
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Tsallis Entropy ◽  
Multilevel Thresholding

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

2020 ◽  
Vol 34 (3) ◽  
pp. 243-256
Author(s):  
Akankshya Das ◽  
Sanjay Agrawal ◽  
Leena Samantaray ◽  
Rutuparna Panda ◽  
Ajith Abraham
Keyword(s):  
Image Analysis ◽  
State Of The Art ◽  
Multilevel Thresholding ◽  
Mr Image

scholarly journals An Interval Iteration Based Multilevel Thresholding Algorithm for Brain MR Image Segmentation

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1429
Author(s):  
Yuncong Feng ◽  
Wanru Liu ◽  
Xiaoli Zhang ◽  
Zhicheng Liu ◽  
Yunfei Liu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Base Layer ◽  
Multilevel Thresholding ◽  
Original Image ◽  
Mr Image ◽  
Interval Iteration ◽  
Otsu Method ◽  
Thresholding Algorithm ◽  
Mr Image Segmentation ◽  
Fusion Scheme

In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid L1 − L0 layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.

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