Evaluation of Particle Swarm Optimisation for Medical Image Segmentation

Ryalat, Mohammad Hashem; Emmens, D.; Hulse, Mark; Bell, Duncan; Al-Rahamneh, Zainab; Laycock, Stephen D.; Fisher, Mark

doi:10.1007/978-3-319-48944-5_6

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…deviation. Mohammad Hashem Ryalat et al [ 23 ] applied PSO, DPSO, and FODPSO algorithms on three brain tumour MR images, performed segmentation and volume reconstruction, and identified tumours that affected the head and neck. FODPSO has performed better than others in terms of speed, accuracy, and stability.…”

Section: Introductionmentioning

confidence: 99%

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

Shivahare

Gupta²

2022

Journal of Healthcare Engineering

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This article addresses automated segmentation and classification of COVID-19 and normal chest CT scan images. Segmentation is the preprocessing step for classification, and 12 DWT-PCA-based texture features extracted from the segmented image are utilized as input for the random forest machine-learning algorithm to classify COVID-19/non-COVID-19 disease. Diagnosing COVID-19 disease through an RT-PCR test is a time-consuming process. Sometimes, the RT-PCR test result is not accurate; that is, it has a false negative, which can cause a threat to the person’s life due to delay in starting the specified treatment. At this moment, there is an urgent need to develop a reliable automatic COVID-19 detection tool that can detect COVID-19 disease from chest CT scan images within a shorter period and can help doctors to start COVID-19 treatment at the earliest. In this article, a variant of the whale optimization algorithm named improved whale optimization algorithm (IWOA) is introduced. The efficiency of the IWOA is tested for unimodal (F1–F7), multimodal (F8–F13), and fixed-dimension multimodal (F14–F23) benchmark functions and is compared with the whale optimization algorithm (WOA), salp swarm optimization (SSA), and sine cosine algorithm (SCA). The experiment is carried out in 30 trials and population size, and iterations are set as 30 and 100 under each trial. IWOA achieves faster convergence than WOA, SSA, and SCA and enhances the exploitation and exploration phases of WOA, avoiding local entrapment. IWOA, WOA, SSA, and SCA utilized Otsu’s maximum between-class variance criteria as fitness function to compute optimal threshold values for multilevel medical CT scan image segmentation. Evaluation measures such as accuracy, specificity, precision, recall, Gmean, F_measure, SSIM, and 12 DWT-PCA-based texture features are computed. The experiment showed that the IWOA is efficient and achieved better segmentation evaluation measures and better segmentation mask in comparison with other methods. DWT-PCA-based texture features extracted from each of the 160 IWOA-, WOA-, SSA-, and SCA-based segmented images are fed into random forest for training, and random forest is tested with DWT-PCA-based texture features extracted from each of the 40 IWOA-, WOA-, SSA-, and SCA-based segmented images. Random forest has reported a promising classification accuracy of 97.49% for the DWT-PCA-based texture features, which are extracted from IWOA-based segmented images.

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Section: Introductionmentioning

confidence: 99%

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

Shivahare

Gupta²

2022

Journal of Healthcare Engineering

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“…Kennedy and Eberhart introduced Particle swarm optimization (PSO) (Du and Swamy, 2016), involving simulating behaviors to find the most suitable results. Literature exposes that several PSO optimization strategies in task scheduling (Jamali et al, 2016;Prathibha et al, 2017), medical (Jothi, 2016;Ryalat et al, 2016), oil and gas (Salehi and Goorkani, 2017;Siavashi and Doranehgard, 2017), batik production (Soesanti and Syahputra, 2016) have been positively applied in biochemical processes because of their controlled parameters to solve optimization problems (Liu et al, 2008).…”

Section: Fermentation Strategiesmentioning

confidence: 99%

Bacterial Pigments: Sustainable Compounds With Market Potential for Pharma and Food Industry

Venil

Dufossé

Devi

2020

Front. Sustain. Food Syst.

106

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The continued universal application of synthetic colorants for decades have caused environmental pollutions and human health vulnerabilities. So, it was indispensable to discover novel natural colorants such as microbial colorants which were safer and better than synthetic colorants. The potential of bacterial pigments for mass production of diversified coloring properties was first prospective and is now getting the notable importance and attention of both the researchers and industries. Literature establishes that the natural colorants produced from microbes were applied in food and pharma products successfully. Apart from serving as food colorants, bacterial pigments have several pharmacological activities like anti-microbial, anti-cancer, anti-oxidant, anti-inflammatory and anti-allergic properties with large economic potential. And, there is vast scope for easy and cheap production of natural colorants in all seasons from bacterial sources, compared to plant sources. Tactics in strain improvement, fermentation conditions, metabolic engineering, and easy extraction techniques are needed to produce high end products. This review highlights the significance of bacterial colorants and summarizes its application in food and pharma industries. Further, the major challenge of lower stability of bacterial pigments and the solution to address it is also appraised.

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“…A simple modified PSO is proposed by Lee et al (Lee et al, 2012) to extract both low-level features and high-level image semantics from the color image. Tillet et al (Tillett et al, 2005) have introduced Darwinien PSO, Ghamisi et al (Ghamisi et al, 2014) have devised fractional-order Darwinien PSO and these techniques are evaluated on medical images (Ryalat et al, 2016). In the biometrics domain, Parez et al have used PSO to generate the templates for face and iris localization (Perez et al, 2010) and Chen and Chu have combined probabilistic neural network and PSO to design an optimized classifier model for iris recognition (Chen and Chu, 2009).…”

Section: Related Workmentioning

confidence: 99%

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Shekar

Bhat²

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor's series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.

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Evaluation of Particle Swarm Optimisation for Medical Image Segmentation

Cited by 8 publications

References 11 publications

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

Bacterial Pigments: Sustainable Compounds With Market Potential for Pharma and Food Industry

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

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