Tsallis Entropy Based Image Thresholding for Image Segmentation

Naidu, M.S.R.; Kumar, Pardeep

doi:10.1007/978-981-10-3874-7_34

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…It is pertinent to multilevel thresolding has been tried with Ostus's entropy and Kapur's entropy; Tsallis entropy has been preferred for coloured satellite image using differential evaluation (DE) which is capable of high dimensional search space problem. In [10], such results have been compared with wind driven optimization (WDO), ABC and PSO.Very encouraging results have been obtained by Naidu et al [11] is optimizing Tsallis entropy with ACS and modified the initial results obtained through CS in which step of work required to be made adaptive for attaining global maximum. They have also tried on similar lines on firefly algorithm [12].…”

Section: Introductionmentioning

confidence: 99%

2-D histogram Based Multilevel Thresholding for Image Segmentation by Hybrid Bacterial Foraging Optimization and Particle Swarm Optimization

Kumar¹,

Reddy²,

Srinivas³

2019

IJITEE

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The objective of image segmentation is to extract meaningful clusters in given image. Meaningful clusters are possible with perfect threshold values which are optimized by assuming Renyi entropy as an objective function. A 1-D histogram based multilevel thresholding is computationally complex and segmented image visual quality comparatively low because of equal distribution of energy over the entire histogram plan. To overcome the problem, a 2-D histogram based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel Hybrid Bacterial Foraging Optimization Algorithm and Particle Swarm Optimization (hBFOA-PSO) and the obtained results are compared with state of art optimization techniques. The results of the proposed model have been evaluated on a standard image dataset. The results obtained after implementing a 2-D histogram suggest hBFOA-PSO can be effectively used won multilevel thresholding problems resulting in a high accuracy.

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Section: Introductionmentioning

confidence: 99%

2-D histogram Based Multilevel Thresholding for Image Segmentation by Hybrid Bacterial Foraging Optimization and Particle Swarm Optimization

Kumar¹,

Reddy²,

Srinivas³

2019

IJITEE

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The Role of D-Summable Information Dimension in Differentiating Covid-19 Disease

2021

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The current COVID-19 pandemic mainly affects the upper respiratory tract. People with COVID-19 report a wide range of symptoms, some of which are similar to those of common flu, such as sore throat and rhinorrhea. Additionally, COVID-19 shares many clinical symptoms with severe pneumonia, including fever, fatigue, dry cough, and respiratory distress. Several diagnostic strategies, such as the real-time polymerase chain reaction technique and computed tomography imaging, which are more costly than chest radiography, are employed as diagnostic tools. The purpose of this paper is to describe the role of the d-summable information dimension of X-ray images in differentiating several lesions and lung illnesses better than both fractal and information dimensions. The statistical analysis shows that the d-summable information dimension model better describes the information obtained from the X-ray images. Therefore, it is a more precise measure of complexity than the information and box-counting dimension. The results also show that the X-ray images of COVID-19 pneumonia reveal greater damage than those of tuberculosis, pneumonia, and various lung lesions, where the damage is minor or much focused. Because the d-summable information dimension increases as the image complexity decreases, it could pave the way to formulate a new measure to quantify the lung damage and assist the clinical diagnosis based on the area under the d-summable information model. In addition, the physical meaning of the [Formula: see text] parameter in the d-summable information dimension is given.

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Evolutionary Image Thresholding for Image Segmentation

N.S.R.

P.V.G.D.

2019

International Journal of Computer Vision and Image Processing

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Image segmentation is a method of segregating the image into required segments/regions. Image thresholding being a simple and effective technique, mostly used for image segmentation, these thresholds are optimized by optimization techniques by maximizing the Tsallis entropy. However, as the two level thresholding extends to multi-level thresholding, the computational complexity of the algorithm is further increased. So there is need of evolutionary and swarm optimization techniques. In this article, first time optimal thresholds are obtained by maximizing the Tsallis entropy by using novel hybrid bacteria foraging optimization technique and particle swam optimization (hBFOA-PSO). The proposed hBFOA-PSO algorithm performance in segmenting the image is tested using natural and standard images. Experiments show that the proposed hBFOA-PSO is better than particle swarm optimization (PSO), the cuckoo search (CS) and the adaptive Cuckoo Search (ACS).

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Tsallis Entropy Based Image Thresholding for Image Segmentation

Cited by 3 publications

References 13 publications

2-D histogram Based Multilevel Thresholding for Image Segmentation by Hybrid Bacterial Foraging Optimization and Particle Swarm Optimization

2-D histogram Based Multilevel Thresholding for Image Segmentation by Hybrid Bacterial Foraging Optimization and Particle Swarm Optimization

The Role of D-Summable Information Dimension in Differentiating Covid-19 Disease

Evolutionary Image Thresholding for Image Segmentation

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