This short l per is mainly the introduction oJ the a ailable INRIA Research Report No. 2050 (55 page,) published by the author in Sept. 1993 . The automated analysis of 3D medical images can improve significantly both diagnosis and therapy. This automation raises a number of new fascinating research problems in the fields of computer vision and robotics. In this paper, I propose a list of such problems after a review of the current major 3D imaging modalities, and a description of the related medical needs. I then present some of past and current work done in the research group EPIDAURE ] at INRIA, on the following topics: segmentation of 3D images, 3D shape modeling, 3D rigid and nonrigid registration, 3D motion analysis and 3D simulation of therapy. I also'try to suggest a number of promising research tracks and future challenges.
Ushizima, Daniela (Lawrence Berkeley National Laboratory) | Manovich, Lev (University of California, San Diego) | Margolis, Todd (University of California, San Diego) | Douglas, Jeremy (Ashford University)
Deluge became a metaphor to describe the amount of information to which we are subjected, and very often we feel we are drowning while our access to information is rising. Devising mechanisms for exploring massive image sets according to perceptual attributes is still a challenge, even more when dealing with user-generated social media content. Such images tend to be heterogenous, and using metadata-only can be misleading. This paper describes a set of tools designed to analyze large sets of user-created art related images using image features describing color, texture, composition and orientation. The proposed pipeline permits to discriminate Flickr groups in terms of feature vectors and clustering parameters. The algorithms are general enough to be applied to other domains in which the main question is about the variability of the images.
While scientists have been learning more and more about our solar system and the way things work, many of our Sun's mechanics still remain a mystery. In advance of the launch of the Parker Solar Probe, which will make contact with the Sun's outer atmosphere, however, scientists are foreshadowing what the spacecraft might see with new discoveries. In a paper published this week in The Astrophysical Journal, scientists detected structures within the Sun's corona, thanks to advanced image processing techniques and algorithms. The question that this group of scientists, led by Craig DeForest from the Southwest Research Institute's branch in Boulder, Colorado, was trying to answer was in regard to the source of solar wind. "In deep space, the solar wind is turbulent and gusty," said DeForest in a release.
This is a curated list of medical data for machine learning. This list is provided for informational purposes only, please make sure you respect any and all usage restrictions for any of the data listed here. The National Library of Medicine presents MedPix Database of 53,000 medical images from 13,000 patients with annotations. These 1112 datasets are composed of structural and resting state functional MRI data along with an extensive array of phenotypic information. Also has clinical, genomic, and biomaker data. AMRG Cardiac Atlas The AMRG Cardiac MRI Atlas is a complete labelled MRI image set of a normal patient's heart acquired with the Auckland MRI Research Group's Siemens Avanto scanner.