206561425
Biomedical Image Analysis
The papers assembled in this special issue of Computer Vision and Image Understanding represent a selection from among those presented at the First IEEE/ SIAM Joint Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA). This meeting represented a blending of the talents and interests of individuals who attended the IEEE Workshop on Biomedical Image Analysis held in Seattle in 1994 and the Mathematical Methods in Medical Imaging Conferences previously held under SPIE sponsorship in San Diego during 1992–1994. The fulllength papers submitted to the workshop were reviewed by at least two members of the program committee and the results were compiled to produce 32 papers presented in a one-track forum on June 21–22, 1996, in San Francisco. In addition, the meeting included two plenary talks by Fred Bookstein and Demetri Terzopoulos. The primary focus of the research presented at the meeting was on the development of new methods for segmentation, registration, shape representation, and motion tracking for biomedical image analysis. The international flavor of these efforts is indicated by the fact that the individuals making the presentations represented France, Japan, Netherlands, Spain, United Kingdom, and the United States. The papers presented in this issue of Computer Vision and Image Understanding focus primarily on the task of extracting information from medical images acquired from X-ray devices, MR images, nuclear medicine scanners, and ultrasound. These are to be distinguished from images of biological structures acquired through light or confocal microscopy or the scanning electron microscope. Analysis of this type of image has expanded rapidly in scope in recent years, driven in part by advances in the parent disciplines of image formation and reconstruction as well as computer vision and shape modeling. However, the expansion owes in part to the steadily larger availability of 3D image data, which provide a playground for volumetric modeling of tissues, without dealing with projection operators encountered in optics. In order to extract information in images in a more automated, accurate, and reproducible way, methods are needed that combine the underlying image physics with explicit or implicit data structures that effectively account for data originating from living tissues. Also, since the objects under investigation in biomedical images represent the large anatomical and biological variability inherent in nature, the researcher is forced to produce methods which can process the degrees of variability in the data. The issue begins with Bookstein’s summary of developments in morphometrics (the multivariate statistics of shape) that are relevant for medical image analysis and image understanding. The second paper, by Okatani and Deguchi, offers a novel approach to the analysis of the endoscopic data increasingly arising in the course of optically guided surgery. The third and fourth papers approach the classic problem of ridge (or skeleton) extraction from medical images. They represent two distinct approaches: one more traditionally algorithmic and one set instead in the context of image evolutions.
46257718
Comparison of Traditional Brain Segmentation Tools with 3D Self-Organizing Maps
Algorithm-assisted 3D MR brain segmentation may be significantly faster than manual methods and produce visually pleasing results. We tested two- and three-dimensional region growing (2DRG and 3DRG) and self-organizing map (SOM) algorithms for segmentation of the cerebral ventricles. The SOM algorithm provides the greatest times savings, 12∶1, over manual segmentation. Concern for reproducibility of algorithm-assisted segmentation motivated an intra-operator comparative study of these and manual segmentation methods. One of us, DK, segmented the cerebral ventricles from S 3D MR-scan data sets three times manually and with the three algorithms. When variability is measured as the shape variance of derived landmarks sets, the three algorithm-assisted methods show less intra-operator variability than manual segmentation. The 2DRG and 3DRG segmentations show more variability than SOM. Of the 4 methods, SOM segmentation requires the fewest operator decisions.
26290427
Knowledge Capture and Reuse for Geo-spatial Imagery Tasks
The continuously increasing amount and availability of geospatial image data is giving rise to problems of information overload in organisations that rely on digital geo-spatial imagery. Intelligent support for relevant image retrieval is needed in order to help manage large geospatial image libraries. Moreover, managing the knowledge implicit in using geo-spatial imagery to address particular tasks is crucial for capturing and making the most effective use of organisational knowledge assets. We are developing case-based knowledge-management support for large geo-spatial image repositories, which incorporates sketch-based querying for image retrieval; image manipulation and annotation tools for highlighting and composing relevant aspects of task-relevant imagery; and automatic context-based querying for retrieving relevant previous task experiences. This paper describes our approach to knowledge capture and reuse through task-based image annotation, and it introduces the environment we are developing for capture and reuse of task knowledge involving geo-spatial imagery.
15894358
Using Collaborative Filtering Data in Case-Based Recommendation
In the context of PTV, an applied recommender system operating in the TV listings domain, we are examining the potential benefits in merging case-based and collaborative filtering (CF) recommendation techniques by developing case-based reasoning (CBR) methods that employ collaborative filtering style ratings profiles directly as cases. Doing so presents a number of challenges, both in applying a case-based perspective to collaborative filtering, and in addressing the sparsity problem that plagues many collaborative filtering systems. This paper expands on earlier CBR views of collaborative filtering, identifies problems and opportunities for similarity maintenance therein, and proposes and evaluates methods for mining and applying new similarity knowledge.
6128799
Case-Based Decision Support for Intelligent Patient Knowledge Management
The majority of healthcare workers in hospitals continue to record, access and update important patient information using paper charts. Disparate patient data (clinical information, laboratory results and medical imagery) is entered by different caregivers and stored at different locations around the hospital. This is a cumbersome, time consuming process that can result in critical medical errors such as documents being mislaid or prescriptions being misinterpreted due to illegible handwriting. Hospitals everywhere are moving to integrate health data sources using electronic health record (EHR) systems as well as taking advantage of the flexibility and speed of wireless computing to improve the quality and reduce the cost of healthcare. We are developing a mobile application that allows doctors to efficiently access accurate real-time patient information at the point-of-care. The system can assist caregivers in automatically searching through very large repositories of previous patient cases as increasingly large hospital databases are making manual searches of such information unfeasible. The system performs computational prognosis by providing decision support for pre-screening of medical diagnosis. A presenting patient's symptoms can be input to a portable device and the application can quickly retrieve the most similar profiles with known diagnoses from large databases which can be used to compare treatments, diagnosis, test results and other information
42745554
Context-Oriented Image Retrieval
In order to help address problems of information overload in digital imagery task domains, we have developed an interactive approach to the capture and reuse of image context information. Our framework models different aspects of the relationship between images and the domain tasks that they support by monitoring the interactive manipulation and annotation of task-relevant imagery. In particular, a strong focus on task context serves to ground image annotations in domain specific goals. This contrasts with prevalent annotation schemes that focus on what individual images contain but that provide no context for which, if any, of those aspects are important to users. Our work attempts to leverage a measure of the user's intentions with regard to tasks that they address. We analyze human-computer interaction information that enables us to infer why image contents are important in a particular context and how specific images have been used to address particular domain goals.
29970366
Multimodal Interaction - Improving Usability and Efficiency in a Mobile GIS Context
The context of mobility raises many issues for GIS applications. Mobile device limitations, including pen input whilst in motion, result in interfaces which are difficult to navigate and interact with. However, comparatively little research has been conducted to address the interface mobility problem for GIS. We are particularly concerned with the limited interaction techniques available to users of mobile GIS which play a primary role in contributing to the complexity of using such an application whilst mobile. Our research focuses on multimodal interfaces as a means to present users with a wider choice of modalities for interacting with GIS applications. The focus of this paper concerns a comprehensive user study which demonstrates the benefits, in terms of usability and efficiency, of a multimodal interface for the CoMPASS mobile GIS which we have developed.