Contributors may also be requested to serve as reviewers for this project. All submitted chapters will be reviewed on a double-blind review basis. Authors will be notified by Decemabout the status of their proposals and sent chapter guidelines.Full chaptersare expected to be submitted by April 14, 2022, and all interested authors must consult theguidelines for manuscript submissions at prior to submission.
Researchers and practitioners are invited to submit on or before December 15, 2021, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. The book covers a wide spectrum of paradigms like segmentation, feature extraction, feature selection, and classification. This book aims to improve the clinical efficiency as well as attempts to investigate the different types of intelligent techniques and an automatic CAD system to get more reliable and accurate diagnostic conclusion, which will provide a second reading to support the medical doctor to avoid incorrect diagnosis and reduce the burden of the medical doctor by automatic analysis of disease from the images. However, the lack of integration between the radiologist and CAD systems restrains the rate of progress as well as obstructs to reach such advancements in clinical use. With advancements in machine learning techniques and CAD systems, the performance of automated analysis of radiological images has improved substantially in the recent era. To enhance the usability of these screening the computer-aided diagnosis (CAD) systems has emerged as an alternative tool for medical image analysis to get reliable and accurate diagnostic conclusions by offering several essential aids and more accuracy during clinical exploration, even in the case limited performance apparatus. The most common image modality or popular screening for tissue characterization are Magnetic resonance imaging (MRI), X-ray(mammography), and Ultrasound (US). These intelligent techniques are also applied in medical images like enhancement, segmentation, feature extraction, feature selection, and classification. Therefore, to extract meaningful information intelligent techniques are applied to image processing.
There is no extraction of meaningful information from those pixel-wise operations. The goal of image processing is to enhance or compress image/video information by using pixel-wise operations. The main objective of using computer vision and intelligent techniques in medical imaging is to provide useful results to help physicians for effective diagnosis and treatment based on objective evidence. Computer vision and intelligent techniques are not only providing cost-effective solutions to the improved health care delivery but also helpful for providing better medical treatment. Several powerful tools are available involving machine learning, pattern recognition, tracking, and reconstruction to bring us much-needed information that is generally not easily available by trained human specialists. Computer vision makes use of texture, shape, contour, and prior knowledge along with contextual information from images and image sequences to provide the information that helps us to have better human understanding.
#Igi 3 images full#
Despite significant advances in high-resolution medical instruments, physicians cannot always obtain the full amount of information directly from the equipment outputs, and a large amount of data cannot be easily exploited without a computer. It incorporates several medical imaging techniques and achieves an important goal for health improvement all over the world. Recent developments in electronic devices have boosted the research in the medical imaging field. All organizations are striving hard to build communication compatibility among the wide range of devices that have operated independently. The healthcare industry is predominantly moving towards affordable, accessible, and quality health care.