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Nature Inspired Problem-Solving Methods in Knowledge Engineering ; 2nd International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007, La Manga del Mar Menor, Spain, June 18-21, 2007, Proceedings, Part II
The second of a two-volume set, this book constitutes the refereed proceedings of the Second International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC 2007, held in La Manga del Mar Menor, Spain in June 2007.
Modern Computational Intelligence Methods for the Interpretation of Medical Images
A detailed description of up-to-date methods used for computer processing and interpretation of medical images is given. The scope of the book include images acquisition, storing with compression, processing, analysis, recognition and also its automatic understanding In introduction general overview of the computer vision methods designed for medical images is presented. Next sources of medical images are presented with their general characteristics. Both traditional (like X-ray) and very modern (like PET) sources of medical images are presented. The main emphasis is placed on such properties of medical images given by particular medical imaging methods which are important form the point of view of its computer processing, analysis and recognition.
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005 ; 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part I
This paper presents a method for classification of medical images, using machine learning and deformation-based morphometry. A morphological representation of the anatomy of interest is first obtained using highdimensional template warping, from which regions that display strong correlations between morphological measurements and the classification (clinical) variable are extracted using a watershed segmentation, taking into account the regional smoothness of the correlation map which is estimated by a crossvalidation strategy in order to achieve robustness to outliers. A Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique is then used to rank computed features from the extracted regions, according to their effect on the leave-one-out error bound. Finally, SVM classification is applied using the best set of features, and it is tested using leave-one-out. The results from a group of 61 brain images of female normal controls and schizophrenia patients demonstrate not only high classification accuracy (91.8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size
Information processing in medical imaging ; 19th International conference, IPMI 2005, Glenwood Springs, CO, USA, July 10-15, 2005, Proceedings
The nineteenth biennial International Conference on Information Processing in Medical Imaging (IPMI) was held July 11–15, 2005 in Glenwood Springs, CO, USA on the Spring Valley campus of the Colorado Mountain College. Following the successful meeting in beautiful Ambleside in England, this year’s conference addressed important recent developments in a broad range of topics related to the acquisition, analysis and application of biomedical images. Interest in IPMI has been steadily growing over the last decade. This is p- tially due to the increased number of researchers entering the ?eld of medical imagingasaresultoftheWhitakerFoundationandtherecentlyformedNational Institute of Biomedical Imaging and Bioengineering. This year, there were 245 full manuscripts submitted to the conference which was twice the number s- mitted in 2003 and almost four times the number of submissions in 2001. Of these papers, 27 were accepted as oral presentations, and 36 excellent subm- sions that could not be accommodated as oral presentations were presented as posters. Selection of the papers for presentation was a di?cult task as we were unable to accommodate many of the excellent papers submitted this year. All accepted manuscripts were allocated 12 pages in these proceedings.
Image Analysis ; 15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-24, 2007, Proceedings
The present volume contains the proceedings of the Scandinavian Conference on Image Analysis, SCIA 2007, held at Hotel Hvide Hus, Aalborg, Denmark, June 10–14, 2007. Initiated in 1979 by Torleiv Orhaug in Sweden, SCIA 2007 represented the 15th in the biennial series of conferences.
Handbook of biomedical image analysis : Vol.3: Registration models
Handbook of Biomedical Image Analysis: Registration Models (Volume III) is dedicated to the algorithms for registration of medical images and volumes. This volume is aimed at researchers and educators in imaging sciences, radiological imaging, clinical and diagnostic imaging, biomedical engineering, physicists covering different medical imaging modalities and researchers in applied mathematics, algorithmic development, computer vision, signal processing, computer graphics and multimedia in general, both in academia and industry.
Cloud-Based Benchmarking of Medical Image Analysis
Presents the VISCERAL project benchmarks for analysis and retrieval of 3D medical images (CT and MRI) on a large scale, which used an innovative cloud-based evaluation approach where the image data were stored centrally on a cloud infrastructure and participants placed their programs in virtual machines on the cloud. The book presents the points of view of both the organizers of the VISCERAL benchmarks and the participants.
Machine learning for biomedical application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ; 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I
The content of thebook covers the current state-of-the-art literature on federated learning applications for cancer research and Vclinical oncology analysis, as well as an overview of the deep learning approaches improving the current standard of care for brain lesions and current neuroimaging challenges. It is also focusing on the accepted BrainLes workshop submissions, is to provide an overview of new advances of medical image analysis in all the aforementioned brain pathologies. It brings together researchers from the medical image analysis domain, neurologists, and radiologists working on at least one of these diseases. The aim is to consider neuroimaging biomarkers used for one disease applied to the other diseases.
Biomedical data mining for information retrieval : Methodologies, techniques, and applications
Discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally.
Artificial intelligence in recognition and classification of astrophysical and medical images
This book presents innovative techniques in Recognition and Classification of Astrophysical and Medical Images. The contents include: Introduction to pattern recognition and classification in astrophysical and medical images. Image standardization and enhancement. Region-based methods for pattern recognition in medical and astrophysical images. Advanced information processing using statistical methods. Feature recognition and classification using spectral method
Advances in conceptual modeling : Challenges and opportunities ; ER 2008 Workshops CMLSA, ECDM, FP-UML, M2AS, RIGiM, SeCoGIS, WISM, Barcelona Spain, October 20-23, 2008. Proceedings
Constitutes the refereed joint proceedings of seven international workshops held in conjunction with the 27th International Conference on Conceptual Modeling, ER 2008, in Barcelona, Spain, in October 2008.
3D Segmentation for medical images (OsteoVision) = التقطيع ثلاثي الأبعاد للصور الطبية
With the increasing integration of AI across various sectors, artificial intelligence (AI) is already playing a significant role in the healthcare industry, and its use is expected to grow further. AI systems used in image processing and computer vision algorithms have shown a significant ability to perform many operations such as segmentation, classification, and detection. This project presents the application of computer vision algorithms in the field of medical imaging for diagnostic, therapeutic, and interventional purposes. This thesis explores the use of several computer vision algorithms to address different pathologies, specifically brain tumors (glioma) (see Appendix A) and knee osteoarthritis (OA), as well as tracking the progression of knee osteoarthritis using the Kellgren and Lawrence (KL) grading system, a common method for classifying the severity of OA into five grades. To achieve the desired impact, the project employs various techniques, including 3D segmentation for brain tumors, 2D segmentation for knee joints, and multinomial classification for determining the severity of knee OA injuries. The primary aims of the project are to enhance diagnostic accuracy, assist in creating treatment plans, provide an assistive tool for healthcare providers to make more informed decisions, leverage AI's capabilities to detect abnormalities that might escape the human eye, and streamline workflow. To facilitate these goals, the project incorporates a user-friendly UI, a website, and a Flutter-based mobile application, enabling healthcare providers to efficiently integrate these tools into their practice and improve patient care.
