Machine Learning Model for Multi-View Visualization of Medical Images. (27th August 2020)

• Record Type: Journal Article
• Authors: Pradhan, Nitesh; Singh Dhaka, Vijaypal; Rani, Geeta; Chaudhary, Himanshu
• Is Part Of: Computer journal. Volume 65:Number 4(2022)
• Page Start: 805
• Page End: 817
• Publication Date: 2020-08-27
• Subjects: bone image -- X-ray -- CGAN -- deep learning -- CT scan -- machine learning; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxaa111
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: 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 'CT slices to $3$ -D convertor' 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.