{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:11:51Z","timestamp":1771701111602,"version":"3.50.1"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,8,27]],"date-time":"2020-08-27T00:00:00Z","timestamp":1598486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Imaging techniques such as X-ray, computerized tomography scan and magnetic resonance imaging are useful in the correct diagnosis of a disease or deformity in the organ. Two-dimensional imaging techniques such as X-ray give a clear picture of simple bone deformity but fail in visualizing multiple fractures in a bone. Moreover, these lack in providing a multi-angle view of a bone. Three-dimensional techniques such as computerized tomography scan and magnetic resonance imaging present a correct orientation of fracture geometry. Computerized tomography scan is a collection of multiple slices of an image. These slices provide a fair idea about a fracture but fail in the measurement of correct dimensions of a fractured fragment and to observe its geometry. It also exposes a patient with carcinogenic radiations. Magnetic resonance imaging induces a strong magnetic field. So, it becomes ineffective for organs containing metallic implants. The high cost of three-dimensional imaging techniques makes them inaccessible for economic weaker section of society. The limitations of two- and three-dimensional imaging techniques motivate researchers to propose an innovative machine learning model \u2018CT slices to $3$-D convertor\u2019 that accepts multiple slices of an image and yields a multi-dimensional view at all possible angles from 0 degree to 360 degree for an input image.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaa111","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T11:16:44Z","timestamp":1597231004000},"page":"805-817","source":"Crossref","is-referenced-by-count":13,"title":["Machine Learning Model for Multi-View Visualization of Medical Images"],"prefix":"10.1093","volume":"65","author":[{"given":"Nitesh","family":"Pradhan","sequence":"first","affiliation":[{"name":"Manipal University Jaipur, Department of Computer Science and Engineering, Jaipur, India, Rajasthan"}]},{"given":"Vijaypal","family":"Singh Dhaka","sequence":"additional","affiliation":[{"name":"Manipal University Jaipur, Department of Computer and Communication Engineering, Jaipur, India, Rajasthan"}]},{"given":"Geeta","family":"Rani","sequence":"additional","affiliation":[{"name":"Manipal University Jaipur, Department of Computer and Communication Engineering, Jaipur, India, Rajasthan"}]},{"given":"Himanshu","family":"Chaudhary","sequence":"additional","affiliation":[{"name":"Manipal University Jaipur, Department of Electronics and Communication Engineering, Jaipur, Rajasthan"}]}],"member":"286","published-online":{"date-parts":[[2020,8,27]]},"reference":[{"key":"2022041811431840000_ref1","article-title":"3d bones segmentation based on ct images visualization","volume":"28","author":"Rahim","year":"2017","journal-title":"Biomed. 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