Advanced Machine Vision Paradigms for Medical Image Analysis
Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs. 2020 Elsevier Inc. All rights reserved.
Gandhi T.; Bhattacharyya S.; De S.; Konar D.; Dey S.
Advanced Machine Vision Paradigms for Medical Image Analysis, pp. 1-289.
Gandhi T., Department of Electrical Engineering, Indian Institute of Technology, Delhi, New Delhi, India; Bhattacharyya S., Christ University, Department of Computer Science and Engineering, Karnataka, Bangalore, India; De S., Cooch Behar Government Engineering College, Department of Computer Science and Engineering, West Bengal, Cooch Behar, India; Konar D., Sikkim Manipal Institute of Technology, Department of Computer Science and Engineering, Sikkim Manipal University, Sikkim, Majitar, India; Dey S., Sukanta Mahavidyalaya, Department of Computer Science, West Bengal, Dhupguri, Jalpaiguri, India