Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization最新文献

{"title":"Breast mass segmentation using mammographic data: a systematic review","authors":"Harmandeep Singh, V. Sharma, Damanpreet Singh","doi":"10.1080/21681163.2023.2219766","DOIUrl":"https://doi.org/10.1080/21681163.2023.2219766","url":null,"abstract":"","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"8 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86505770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CNN-RSVM: a hybrid approach for classification of poikilocytosis using convolutional neural network and radial kernel basis support vector machine","authors":"P. Dhar, K. Suganya Devi, P. Srinivasan","doi":"10.1080/21681163.2023.2219755","DOIUrl":"https://doi.org/10.1080/21681163.2023.2219755","url":null,"abstract":"","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"42 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76147348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic analysis and review of COVID-19 detection techniques using CT image","authors":"J. Ameera Beegom, T. Brindha","doi":"10.1080/21681163.2023.2219750","DOIUrl":"https://doi.org/10.1080/21681163.2023.2219750","url":null,"abstract":"","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"66 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89576122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cancer prognosis with machine learning-based modified meta-heuristics and weighted gradient boosting algorithm","authors":"P. Saranya, P. Asha","doi":"10.1080/21681163.2023.2219772","DOIUrl":"https://doi.org/10.1080/21681163.2023.2219772","url":null,"abstract":"","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"22 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87202417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and classification of COVID-19 disease using SWHO-based deep neural network classifier","authors":"Vanshika Rastogi, A. Jain","doi":"10.1080/21681163.2023.2219767","DOIUrl":"https://doi.org/10.1080/21681163.2023.2219767","url":null,"abstract":"","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"1 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87767571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain tumor classification based on deep CNN and modified butterfly optimization algorithm","authors":"Dr.Vinodkumar Jacob, G. Sagar, Kavita Goura, P. S. S. Pedalanka","doi":"10.1080/21681163.2023.2219754","DOIUrl":"https://doi.org/10.1080/21681163.2023.2219754","url":null,"abstract":"","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"13 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74161109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for few-shot white blood cell image classification and feature learning","authors":"Yixiang Deng, He Li","doi":"10.1080/21681163.2023.2219341","DOIUrl":"https://doi.org/10.1080/21681163.2023.2219341","url":null,"abstract":"","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"43 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81621671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicted Microscopic Cortical Brain Images for Optimal Craniotomy Positioning and Visualization.","authors":"Nazim Haouchine,&nbsp;Pariskhit Juvekar,&nbsp;Alexandra Golby,&nbsp;Sarah Frisken","doi":"10.1080/21681163.2020.1834874","DOIUrl":"https://doi.org/10.1080/21681163.2020.1834874","url":null,"abstract":"<p><p>During a craniotomy, the skull is opened to allow surgeons to have access to the brain and perform the procedure. The position and size of this opening are chosen in a way to avoid critical structures, such as vessels, and facilitate the access to tumors. Planning the operation is done based on pre-operative images and does not account for intra-operative surgical events. We present a novel image-guided neurosurgical system to optimize the craniotomy opening. Using physics-based modeling we define a cortical deformation map that estimates the displacement field at candidate craniotomy locations. This deformation map is coupled with an image analogy algorithm that produces realistic synthetic images that can be used to predict both the geometry and the appearance of the brain surface before opening the skull. These images account for cortical vessel deformations that may occur after opening the skull and is rendered in a way that increases the surgeon's understanding and assimilation. Our method was tested retrospectively on patients data showing good results and demonstrating the feasibility of practical use of our system.</p>","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"9 4","pages":"407-413"},"PeriodicalIF":1.6,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/21681163.2020.1834874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39541805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Developments on Computational Methods and Imaging in Biomechanics and Biomedical Engineering","authors":"J. Tavares, P. Fernandes, F. Engenharia","doi":"10.1007/978-3-030-23073-9","DOIUrl":"https://doi.org/10.1007/978-3-030-23073-9","url":null,"abstract":"","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"27 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84840892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A marker-free registration method for standing X-ray panorama reconstruction for hip-knee-ankle axis deformity assessment.","authors":"Yehuda K Ben-Zikri,&nbsp;Ziv R Yaniv,&nbsp;Karl Baum,&nbsp;Cristian A Linte","doi":"10.1080/21681163.2018.1537859","DOIUrl":"https://doi.org/10.1080/21681163.2018.1537859","url":null,"abstract":"<p><p>Accurate measurement of knee alignment, quantified by the hip-knee-ankle (HKA) angle (varus-valgus), serves as an essential biomarker in the diagnosis of various orthopaedic conditions and selection of appropriate therapies. Such angular deformities are assessed from standing X-ray panoramas. However, the limited field-of-view of traditional X-ray imaging systems necessitates the acquisition of several sector images to capture an individual's standing posture, and their subsequent 'stitching' to reconstruct a panoramic image. Such panoramas are typically constructed manually by an X-ray imaging technician, often using various external markers attached to the individual's clothing and visible in two adjacent sector images. To eliminate human error, user-induced variability, improve consistency and reproducibility, and reduce the time associated with the traditional manual 'stitching' protocol, here we propose an automatic panorama construction method that only relies on anatomical features reliably detected in the images, eliminating the need for any external markers or manual input from the technician. The method first performs a rough segmentation of the femur and the tibia, then the sector images are registered by evaluating a distance metric between the corresponding bones along their medial edge. The identified translations are then used to generate the standing panorama image. The method was evaluated on 95 patient image datasets from a database of X-ray images acquired across 10 clinical sites as part of the screening process for a multi-site clinical trial. The panorama reconstruction parameters yielded by the proposed method were compared to those used for the manual panorama construction, which served as gold-standard. The horizontal translation differences were 0:43 ± 1:95 mm 0:26 ± 1:43 mm for the femur and tibia respectively, while the vertical translation differences were 3:76 ± 22:35 mm and 1:85 ± 6:79 mm for the femur and tibia, respectively. Our results showed no statistically significant differences between the HKA angles measured using the automated vs. the manually generated panoramas, and also led to similar decisions with regards to the patient inclusion/exclusion in the clinical trial. Thus, the proposed method was shown to provide comparable performance to manual panorama construction, with increased efficiency, consistency and robustness.</p>","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"7 4","pages":"464-478"},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/21681163.2018.1537859","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37324987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}