Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques - Santosh, K.C.; Antani, Sameer; Guru, D.S.; Dey, Nilanjan (CRC Press, 2020)
Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging. 1. A novel stacked model ensemble for improved Tuberculosis (TB) detection in chest radiographs Sivaramakrishnan Rajaraman, Sema Candemir, Zhiyun Xue, Philip Alderson, George Thoma, Sameer Antani 2. The Role of Artificial Intelligence (AI) in Medical Imaging: General Radiologic and Urologic Applications Diboro Kanabolo, Mohan S. Gundeti, 3. Early Detection of Epileptic Seizures based on scalp EEG signals Abhishek Agrawal, Lalit Garg, Eliazar Elisha Audu, Ram Bilas Pachori, Justin HG Dauwels 4. Fractal analysis in histology classification of non-small cell lung cancer Ravindra Patil, Geetha M , Srinidhi Bhat, Dinesh M S, Leonard Wee and Andre Dekker 5. Multi feature-based classification of osteoarthritis in knee joint x ray images Ravindra S. Hegadi, Dattatray N. Navale, Trupti D. Pawar, Darshan D. Ruikar 6. Detection and classification of non-proliferative Diabetic Retinopathy Lesions Ramesh R Manza, Bharti W. Gawali, Pravin Yannawar, K.C. Santosh 7. Segmentation and analysis of CT images for bone fracture detection and labelling Darshan D. Ruikar, K.C. Santosh, Ravindra S. Hegadi 8. 3D imaging in biomedical applications: a systematic review Darshan D. Ruikar, Dattatray D. Sawat, K.C. Santosh, Ravindra S. Hegadi 9. Evolution of Digital sliding of pathology in medical imaging M Ravi, Ravindra S Hegadi 10. Pathological medical image segmentation: a quick review based on parametric techniques M Ravi, Ravindra S Hegadi Dr. K.C. Santosh is an Assistant Professor and Graduate Program Coordinator (GPC) of the department of computer science at the University of South Dakota (USD). Also, Dr. Santosh serves School of Computing and IT, Taylor's University as an Adjunct Associate Professor. Before joining USD, Dr. Santosh worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a postdoctoral research scientist at the LORIA research centre, Universite de Lorraine in direct collaboration with industrial partner ITESOFT, France. He also worked as a research scientist at the INRIA Nancy Grand Est research centre, France, where, in just 3 years, he has received his PhD diploma in computer science. Before that, he worked as a graduate research scholar at the SIIT, Thammasat University, Thailand. Dr. Santosh demonstrated expertise in artificial intelligence, machine learning, computer vision, pattern recognition and image processing with various applications in medical image analysis, graphics recognition, document information content exploitation, biometrics and forensics. He published more than 120 peer-reviewed research articles; two authored books (Springer) and edited several books (Springer, Elsevier and CRC press), journal issues (Springer) and conference proceedings (Springer). Dr. Santosh serves as an associate editor of the International Journal of Machine Learning & Cybernetics (Springer). For more information, visit: kc-santosh.org. Sameer Antani , PhD is a versatile researcher leading several scientific and technical research projects. He applies his expertise in biomedical image informatics, automatic medical image interpretation, machine learning, information retrieval, computer vision, and related topics in computer science and engineering technology toward advancing the role of computational sciences in biomedical research, education, and clinical care. His current R&D projects include: an　automatic screening system　for detecting presence of Tuberculosis (TB) and other pulmonary abnormalities in digital chest x-ray images; an automatic cell counting system for　malaria　screening; retrieval of fMRI data based on activation similarity; and, the　OPEN-i SM 　biomedical image retrieval system that provides text and visual search capability to retrieve over 3.2 million images and videos from approximately 1.2 million Open Access biomedical research articles from NLM’s　PubMed Central®　repository.His other work includes contributions to cervical cancer diagnostics through cervicography and histology image analysis; retrieval of spine x-rays from an image database using visual and shape queries; and, next generation scientific publishing. Dr. Antani is a Senior Member of the　International Society of Photonics and Optics (SPIE), Institute of Electrical and Electronics Engineers (IEEE)　and the　IEEE Computer Society. He　serves as the Vice Chair for Computational Medicine on the　IEEE Technical Committee on Computational Life Sciences (TCCLS), and as an Associate Editor for　the IEEE Journal of Biomedical and Health Informatics