Computer vision for biomedical image applications first international workshop, CVBIA 2005, Beijing, China, October 21, 2005. proceedings - CVBIA 2005 (Image processing, computer vision, pattern recognition, and graphics, 2005)
With the rapid increase in the variety and quantity of biomedical images in recent years, we see a steadily growing number of computer vision technologies applied to biomedical applications. The time is ripe for us to take a closer look at the accomplishments and experiences gained in this research subdomain, and to strategically plan the directions of our future research. The scientific goal of our workshop, “Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends” (CVBIA), is to examine the diverse applications of computer vision to biomedical image applications, considering both current methods and promising new trends. An additional goal is to provide the opportunity for direct interactions between (1) prominent senior researchers and young scientists, including students, postdoctoral associates and junior faculty; (2) local researchers and international leaders in biomedical image analysis; and (3) computer scientists and medical practitioners. Our CVBIA workshop had two novel characteristics: each contributed paper was authored primarily by a young scientist, and the workshop attracted an unusually large number of well-respected invited speakers (and their papers). We had the good fortune of having Dr. Ayache of INRIA, France to talk about “Computational Anatomy and Computational Physiology,” Prof. Grimson of MIT to discuss “Analyzing Anatomical Structures: Leveraging Multiple Sources of Knowledge,” Dr. Jiang of the Chinese Academy of Sciences to present their work on “Computational Neuroanatomy and Brain Connectivity,” Prof. Computational anatomy and computational physiology for medical image analysis - Analyzing anatomical structures: leveraging multiple sources of knowledge - Advances on medical imaging and computing - Tracking of migrating and proliferating cells in phase-contrast microscopy imagery for tissue engineering - Cardiology meets image analysis: just an application or can image analysis usefully impact cardiology practice? - Computer vision algorithms for retinal image analysis: current results and future directions - 3D statistical shape models to embed spatial relationship information - A generalized level set formulation of the mumford-shah functional with shape prior for medical image segmentation - A hybrid eulerian-lagrangian approach for thickness, correspondence, and gridding of nnnular tissues - A hybrid framework for image segmentation using probabilistic integration of heterogeneous constraints - A learning framework for the automatic and accurate segmentation of cardiac tagged MRI images - A local adaptive algorithm for microaneurysms detection in digital fundus images - A new coarse-to-fine framework for 3D brain MR image registration - A new vision approach for local spectrum features in cervical images via 2D method of geometric restriction in frequency domain - Active contours under topology control genus preserving level sets - A novel multifaceted virtual craniofacial surgery scheme using computer vision - A novel unsupervised segmentation method for MR brain images based on fuzzy methods - A pattern classification approach to aorta calcium scoring in radiographs - A topologically faithful, tissue-guided, spatially varying meshing strategy for computing patient-specific head models for endoscopic pituitary surgery simulation - Applying prior knowledge in the segmentation of 3D complex anatomic structures - Automatic extraction of femur contours from hip X-Ray images...