Modified demons registration for highly deformed medical images

Mishra, Bimal Kumar; Pati, Umesh Chandra; Sinha, Usna

doi:10.1109/iciip.2015.7414757

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…In 2018, Li et al [ 4 ] implemented a multimodal and multivendor retinal image registration using deformable registration based on modality independent neighborhood descriptor (MIND) framework on color fundus images. To improve the demons registration of medical images, Mishra et al [ 46 ] used the 2D image registration method on mono-modal images. To obtain the deformation fields of medical images, the velocity field of demons was evaluated.…”

Section: Literature Surveymentioning

confidence: 99%

“…The current work focuses on reducing the registration error as well as the time complexity in order to get rid of the problem raised by processing on large chunk of images in retinal image database, that takes a large amount of time. A comparative study is conducted with the cuckoo search- [ 10 ], firefly algorithm [ 43 ], and particle swarm optimization- [ 9 ] based demons registration [ 44 , 45 , 46 ] framework.…”

Section: Introductionmentioning

confidence: 99%

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Grey-Wolf-Based Wang’s Demons for Retinal Image Registration

Chakraborty

Pradhan

Ashour

et al. 2020

Entropy

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Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO)-based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization-based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36 × 10−5.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Grey-Wolf-Based Wang’s Demons for Retinal Image Registration

Chakraborty

Pradhan

Ashour

et al. 2020

Entropy

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“…In the current work, the Grey wolf optimization (GWO) algorithm [40,41] is applied to build up the optimization framework to achieve an optimal solution [42] for better accuracy and faster image non-rigid demons registration [11,[22][23][24][25][26]. A comparative study is conducted with the cuckoo search- [10], firefly algorithm [43] and particle swarm optimization- [9] based demons registration [44,45,46] framework.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Based Wang’s Demons for Retinal Image Registration

Chakraborty¹,

Pradhan²,

Ashour³

et al. 2020

Preprint

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Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO) based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization- based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36×10-5.

show abstract