Machine Learning and AI Techniques in Interactive Medical Image Analysis
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Machine Learning and AI Techniques in Interactive Medical Image Analysis

Lipismita Panigrahi (OP Jindal University, India), Sandeep Biswal (OP Jindal University, India), Akash Kumar Bhoi (Sikkim Manipal Institute of Technology (SMIT), India), Akhtar Kalam (Victoria University, Australia) and Paolo Barsocchi (Institute of Information Science and Technologies, Italy)
Pages: 300
DOI: 10.4018/978-1-6684-4671-3
ISBN13: 9781668446713|ISBN10: 1668446715|EISBN13: 9781668446737|ISBN13 Softcover: 9781668446720
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Description & Coverage
Description:

The healthcare industry is moving towards affordable, accessible, and quality health care. All organizations are striving hard to build communication compatibility among the wide range of devices that have operated independently. Recent developments in electronic devices have boosted the research in the medical imaging field. It incorporates several medical imaging techniques and achieves an important goal for health improvement all over the world. 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. 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.

Several powerful tools are available involving machine learning, pattern recognition, tracking, and reconstruction to bring us much-needed information that is not easily available by trained human specialists. 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. 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.

This book introduces segmentation techniques to locate the suspicious areas in images and increase the segmentation accuracy for medical images. It also explains the importance of appropriate features, which can classify medical images and demonstrate high information packing properties. This book selects the most relevant features and reports some classification techniques to classify the medical images into different classes. Students, researchers, medical doctors, and the professionals who work in the field of computer engineering, machine learning, artificial intelligence, soft computing technologies, image processing, medical imaging, and the related fields can benefit from this handbook by being able to handle real-time challenges in healthcare.

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