Joint method using Akamatsu and discrete wavelet transform for image restoration

Akbar, Jihad Maulana; Setiadi, De Rosal Ignatius Moses

doi:10.1016/j.aci.2019.10.002

Cited by 4 publications

(3 citation statements)

References 29 publications

(80 reference statements)

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“…(ii) Follower's update process: This follows the location of the leading rider for updating the follower's position. It depends on the co-ordinate selector so the location update is done to the chosen values in DM and the mathematical formula is shown in Equation (35). Here, the coordinate selector is denoted as cse and the term dst tm p is the distance traveled by pth rider.…”

Section: Update the Rider's Positionmentioning

confidence: 99%

“…Moreover, the wavelet decomposition is also useful in identifying singularity information by evaluating the time frequencies of nonstationary signals. DWT 35 has several beneficial features like space‐frequency localization property, multi‐resolution ability, which makes DWT for implementing to the whole image and can be rebuilt in multiple image sizes. The advantages of DWT are that it becomes much easier to filter in or filter out a nonstationary waveform.…”

Section: Blood Vessel Segmentation and Feature Extraction Of Retinal ...mentioning

confidence: 99%

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Optimized hybrid classifier for diagnosing diabetic retinopathy: Iterative blood vessel segmentation process

Kadan

Subbian

2020

Int J Imaging Syst Tech

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In general, diabetic retinopathy is a hurdle of diabetes that subsists throughout the world. Early detection of this severe disease through computer‐assisted diagnosis tools followed by the right treatment at the right time could control its terrible condition. From the last 2 years, numerous research efforts in this area have been introduced for the automatic detection of diabetic retinopathy with appropriate evaluations. However, there is a large variability in the databases and evaluation criteria used in the literature. Accordingly, this proposal tactics to develop a new contribution to automatic detection of diabetic retinopathy based on four main stages: “(a) image pre‐processing, (b) blood vessels segmentation, (c) feature extraction and dimension reduction, and (d) diabetic retinopathy recognition”. Two steps are used for accomplishing the image pre‐processing, (a) conversion of RGB into green channel image and (b) noise removal by median filtering. Further, the pre‐processed fundus image is subjected to Iterative segmentation‐based blood vessel segmentation. For performing the precise classification of the images, there is a prerequisite to extract the relevant informative features from the segmented blood vessels. Here, the features are extracted using discrete wavelet transform, and gray‐level co‐occurrence matrix. To attain the unique features with different information, the dimension reduction process is applied using principle component analysis. Finally, the Diabetic Retinopathy recognition is performed enabling a hybrid classifier, which merges the beneficial concepts of neural network, and convolutional neural network. As the main novelty, the number of hidden neurons in both neural network and convolutional neural network is optimized by the modified rider optimization algorithm called improvement counter‐based rider optimization algorithm intending to maximize the diagnostic accuracy. Moreover, convolutional neural network takes the transformed form of the segmented blood vessels using Discrete Wavelet Transform as input, and Neural Network takes dimension reduced features as input, and AND‐bit operation of the both classified outputs provides the diagnostic results, whether the corresponding image is normal or abnormal.

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Section: Update the Rider's Positionmentioning

confidence: 99%

Section: Blood Vessel Segmentation and Feature Extraction Of Retinal ...mentioning

confidence: 99%

Optimized hybrid classifier for diagnosing diabetic retinopathy: Iterative blood vessel segmentation process

Kadan

Subbian

2020

Int J Imaging Syst Tech

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“…Numerous business cases and applications may require strict full local (private) control over the data and infrastructure [11][12][13][14], yet may pose substantial computing demands, e.g. for document management [15,16], image processing [17,18], surveillance applications [19], education system management [20,21] and process simulation [22]. This is where the private cloud can come into play for both IaaS and PaaS.…”

Section: Introductionmentioning

confidence: 99%

Design of a small-scale and failure-resistant IaaS cloud using OpenStack

Heuchert

Rimal

Reisslein

et al. 2021

ACI

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PurposeMajor public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.Design/methodology/approachTo demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.FindingsThrough the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.Originality/valueThe authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

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