Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Grey Wolf Optimization Guided Non-Local Means Denoising for Localizing and Extracting Bone Regions from X-Ray Images Subject The topic of the resource Convolutional neural network; Grey wolf optimization; Multi-Res U-Net; Non-local means denoising; Semantic segmentation Description An account of the resource The key focus of the current study is implementation of an automated semantic segmentation model to localize and extract bone regions from digital X-ray images. Methods: The proposed segmentation framework uses a pre-processing stage which follows convolutional neural network (CNN) obtained segmentation stage to extract the bone region from X-ray images, mainly for diagnosing critical conditions such as osteoporosis. Since the presence of noise is critical in image analysis, the X-ray images are initially processed with a grey wolf optimization (GWO) guided non-local means (NLM) denoising. The segmentation stage uses a Multi-Res U-Net architecture with attention modules. Findings: The proposed methodology shows superior results while segmenting bone regions from real X-ray images. The experiments include an ablation study that substantiates the need for the proposed denoising approach. Several standard segmentation benchmarks such as precision, recall, Dice-score, specificity, Intersection over Union (IOU), and total accuracy have been used for a comprehensive study. The proposed architectural has good impact compared to the state-of-the-art bone segmentation models and is compared both quantitatively and qualitatively. Novelty: The denoising using GWO-NLM adaptively chose the denoising parameters based on the required conditions and can be reused in other medical image analysis domains with minimal finetuning. The design of the proposed CNN model also aims at better performance on the target datasets. 2023 Biomedical &amp; Pharmacology Journal. Creator An entity primarily responsible for making the resource Dodamani P.S.; Danti A. Source A related resource from which the described resource is derived Biomedical and Pharmacology Journal, Vol-16, No. 2, pp. 935-946. Publisher An entity responsible for making the resource available Oriental Scientific Publishing Company Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13005/bpj/2676" target="_blank" rel="noreferrer noopener">https://doi.org/10.13005/bpj/2676</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165669738&amp;doi=10.13005%2Fbpj%2F2676&amp;partnerID=40&amp;md5=03e010fb60b5a09bc1e013628f96263e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165669738&amp;doi=10.13005%2fbpj%2f2676&amp;partnerID=40&amp;md5=03e010fb60b5a09bc1e013628f96263e</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 9746242 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dodamani P.S., CSE, Christ University, Bangalore, 560057, India; Danti A., CSE, Christ University, Bangalore, 560057, India