Skip to main content

Advanced Machine Vision Paradigms for Medical Image Analysis

  • 1st Edition - August 11, 2020
  • Editors: Tapan K. Gandhi, Siddhartha Bhattacharyya, Sourav De, Debanjan Konar, Sandip Dey
  • Language: English
  • Paperback ISBN:
    9 7 8 - 0 - 1 2 - 8 1 9 2 9 5 - 5
  • eBook ISBN:
    9 7 8 - 0 - 1 2 - 8 1 9 2 9 6 - 2

Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation th… Read more

Advanced Machine Vision Paradigms for Medical Image Analysis

Purchase options

Limited Offer

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

Book bundle cover eBook and print

Institutional subscription on ScienceDirect

Request a sales quote

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.