Image Segmentation Based on Galactic Swarm Optimization

Chakraborty, Shouvik; Mali, Kalyani; Banerjee, Arghasree; Bhattacharjee, Mayukh; Chatterjee, Sankhadeep

doi:10.1007/978-981-15-9433-5_24

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Kapur’s entropy is used as a fitness function that needs to be maximized to reach the optimum threshold values. Furthermore, a new meta-heuristic algorithm, galactic swarm optimization, has been adapted to tackle image segmentation [ 52 ]. Anitha et al [ 53 ] introduced a modified WOA to maximize Otsu’s and Kapur’s objective functions to enhance the threshold selection for MLT of color images.…”

Section: Literature Reviewmentioning

confidence: 99%

Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm

Alsahafi,

Elshora,

Mohamed

et al. 2023

Diagnostics

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Skin Cancer (SC) is among the most hazardous due to its high mortality rate. Therefore, early detection of this disease would be very helpful in the treatment process. Multilevel Thresholding (MLT) is widely used for extracting regions of interest from medical images. Therefore, this paper utilizes the recent Coronavirus Disease Optimization Algorithm (COVIDOA) to address the MLT issue of SC images utilizing the hybridization of Otsu, Kapur, and Tsallis as fitness functions. Various SC images are utilized to validate the performance of the proposed algorithm. The proposed algorithm is compared to the following five meta-heuristic algorithms: Arithmetic Optimization Algorithm (AOA), Sine Cosine Algorithm (SCA), Reptile Search Algorithm (RSA), Flower Pollination Algorithm (FPA), Seagull Optimization Algorithm (SOA), and Artificial Gorilla Troops Optimizer (GTO) to prove its superiority. The performance of all algorithms is evaluated using a variety of measures, such as Mean Square Error (MSE), Peak Signal-To-Noise Ratio (PSNR), Feature Similarity Index Metric (FSIM), and Normalized Correlation Coefficient (NCC). The results of the experiments prove that the proposed algorithm surpasses several competing algorithms in terms of MSE, PSNR, FSIM, and NCC segmentation metrics and successfully solves the segmentation issue.

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Section: Literature Reviewmentioning

confidence: 99%

Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm

Alsahafi,

Elshora,

Mohamed

et al. 2023

Diagnostics

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Fuzzy and elitist cuckoo search based microscopic image segmentation approach

Chakraborty

Mali

2022

Applied Soft Computing

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A morphology-based radiological image segmentation approach for efficient screening of COVID-19

Chakraborty

Mali

2021

Biomedical Signal Processing and Control

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Computer-aided radiological image interpretation systems can be helpful to reshape the overall workflow of the COVID-19 diagnosis process. This article describes an unsupervised CT scan image segmentation approach. This approach begins by performing a morphological reconstruction operation that is useful to remove the effect of the external disturbances on the infected regions and to locate different regions of interest precisely. The optimal size of the structuring element is selected using the Edge Content-based contrast matrix approach. After performing the opening by using the morphological reconstruction operation, further noise is eliminated using the closing-based morphological reconstruction operation. The original pixel space is restored and the obtained image is divided into some non-overlapping smaller blocks and the mean intensity value for each block is computed that is used as the local threshold value for the binarization purpose. It is preferable to manually determine the range of the infected region. If a region is greater than the upper bound then that region will be considered as an exceptional region and processed separately. Three standard metrics MSE, PSNR, and SSIM are used to quantify the outcomes. Both quantitative and qualitative comparisons prove the efficiency and real-life adaptability of this approach. The proposed approach is evaluated with the help of 400 different images and on average, the proposed approach achieves MSE 307.1888625, PSNR 23.7246505, and SSIM 0.831718459. Moreover, the comparative study shows that the proposed approach outperforms some of the standard methods and obtained results are encouraging to support the battle against the COVID-19.

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Image Segmentation Based on Galactic Swarm Optimization

Cited by 4 publications

References 19 publications

Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm

Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm

Fuzzy and elitist cuckoo search based microscopic image segmentation approach

A morphology-based radiological image segmentation approach for efficient screening of COVID-19

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