A comparison of stochastic optimization techniques for image segmentation

Bhandarkar, Suchendra M.; Hui, Zhang

doi:10.1002/(sici)1098-111x(200005)15:5<441::aid-int4>3.0.co;2-r

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…Medical image segmentation is the process of separating regions or structures of interest from medical images and plays an important role in computer-aided clinical diagnosis and treatment. Traditional medical image segmentation algorithms mainly include banalization methods (39), watershed algorithms (40), level set-based methods (41), and stochastic optimization models (42). To improve the accuracy of segmentation, curvature constraints and local feature constraints are often added into the models.…”

Section: Resultsmentioning

confidence: 99%

Medical applications of generative adversarial network: a visualization analysis

Zhang,

Wang,

Zhao

et al. 2023

Acta Radiol

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Background Deep learning (DL) is one of the latest approaches to artificial intelligence. As an unsupervised DL method, a generative adversarial network (GAN) can be used to synthesize new data. Purpose To explore GAN applications in medicine and point out the significance of its existence for clinical medical research, as well as to provide a visual bibliometric analysis of GAN applications in the medical field in combination with the scientometric software Citespace and statistical analysis methods. Material and Methods PubMed, MEDLINE, Web of Science, and Google Scholar were searched to identify studies of GAN in medical applications between 2017 and 2022. This study was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Citespace was used to analyze the number of publications, authors, institutions, and keywords of articles related to GAN in medical applications. Results The applications of GAN in medicine are not limited to medical image processing, but will also penetrate wider and more complex fields, or may be applied to clinical medicine. Eligibility criteria were the full texts of peer-reviewed journals reporting the application of GANs in medicine. Research selections included material published in English between 1 January 2017 and 1 December 2022. Conclusion GAN has been fully applied to the medical field and will be more deeply and widely used in clinical medicine, especially in the field of privacy protection and medical diagnosis. However, clinical applications of GAN require consideration of ethical and legal issues. GAN-based applications should be well validated by expert radiologists.

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Section: Resultsmentioning

confidence: 99%

Medical applications of generative adversarial network: a visualization analysis

Zhang,

Wang,

Zhao

et al. 2023

Acta Radiol

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“…Murino and Trucco put forward a technique based on Markov random fields methodology and used SA to minimize the energy functional to get the optimal estimates of underwater acoustic images (Murino and Trucco, 1998). Bhandarkar and Zhang used SA, micro-canonical annealing (MCA) and the random cost algorithm (RCA) for the minimization of a cost function based on edge information and region gray-scale variances (Bhandarkar and Zhang, 2000). Recently, Qureshi et al introduced a hybrid simulated annealing (HSA), which has been found effective for parallel-ray tomographic image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Determination of optimal number of projections and parametric sensitivity analysis of operators for parallel‐ray transmission tomography using hybrid continuous genetic algorithm

Qureshi

Mirza

Arif

2007

Int J Imaging Syst Tech

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In this work, a hybrid continuous genetic algorithm (HCGA) based methodology has been developed for optimization of number of projections for parallel-ray transmission tomography. The HCGA calculations with filtered back-projection (FBP) utilize 8 bits for both head and lung phantoms. The effect of selection operator through proportionate, truncation, and tournament schemes has been analyzed along with the introduction of a mixed-selection scheme. Image quality has been measured using root-mean-squared error, Euclidean error and peak signal-to-noise ratios. The sensitivity of reconstructed image quality on various mutation operators, namely standard, gradient-, and offset-based schemes, has been analyzed along with the effect of number of projections. The number of projections has resulted in maximization of image quality while minimizing the radiation hazard involved. The results of HCGA have been compared with FBP as a deterministic technique and simulated annealing (SA) as a stochastic technique for IRT approximation. For the 8 Â 8 head and lung phantoms, HCGA, SA, FBP, have resulted PSNR values correspondingly as 40.47, 33.92, 8.28 and 26.38, 20.36, 12.98 dB.

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