Microscopic Image Segmentation Using Quantum Inspired Evolutionary Algorithm

Choudhury, Alokeparna; Samanta, Sourav; Dey, Nilanjan; Ashour, Amira S.; Balas-Timar, Dana; Gospodinov, Mitko; Gospodinova, Еvgeniya

doi:10.1166/jamr.2015.1257

Cited by 9 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A microscopic image segmentation using a quantum inspired evolutionary algorithm has been proposed in Ref. [70]. It is evident that image segmentation has become an important research area for various medical imaging fields.…”

Section: Medical Image Segmentationmentioning

confidence: 99%

Quantum medical images processing foundations and applications

Elaraby

2022

IET Quantum Communication

View full text Add to dashboard Cite

Medical imaging is considered one of the most important areas within scientific imaging due to the rapid and ongoing development in computer‐aided medical image visualisation, advances in analysis approaches, and computer‐aided diagnosis. Here, the principles of quantum computation and information to develop the field of medical image processing will be reviewed. The advancement of quantum computation in image processing has proved its outstanding properties for processing and storage capacity compared to the classical methods. This review provides a comprehensive summary of the common advanced approaches, methodologies and advanced applications in medical images based on quantum computation.

show abstract

Section: Medical Image Segmentationmentioning

confidence: 99%

Quantum medical images processing foundations and applications

Elaraby

2022

IET Quantum Communication

View full text Add to dashboard Cite

show abstract

“…MSE and PSNR are measured to understand the segmentation quality. References [28, 29] discussed several medical applications using metaheuristic-based approaches for segmentation, and a novel approach to deal with rats microscopic hippocampus images segmentation based on the hybrid evolutionary strategy (ES) is proposed. The results have superior segmentation with eight levels.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering

Zhu

et al. 2018

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

In this paper, we propose a novel algorithm for medical image segmentation, which combines the density peaks clustering (DPC) with the fruit fly optimization algorithm, and it has the following advantages. Firstly, it avoids the problem of DPC that needs to artificially select parameters (such as the number of clusters) in its decision graph and thus can automatically determine their values. Secondly, our algorithm uses random step size, instead of the fixed step size as in the fruit fly optimization algorithm, which helps avoid falling into local optima. Thirdly, our algorithm selects the cut-off distance and the cluster centers using the image entropy value and can better capture the structures of the image. Experiments on benchmark dataset and proprietary dataset show that our algorithm can adaptively segment medical images with faster convergence and better robustness.

show abstract

Image Inpainting for Irregular Holes Using Extreme Learning Machine

Kanhar¹,

Chandak²

2020

Studies in Computational Intelligence

View full text Add to dashboard Cite

Microscopic Image Segmentation Using Quantum Inspired Evolutionary Algorithm

Cited by 9 publications

References 0 publications

Quantum medical images processing foundations and applications

Quantum medical images processing foundations and applications

Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering

Image Inpainting for Irregular Holes Using Extreme Learning Machine

Contact Info

Product

Resources

About