Journal ArticleDOI
Knowledge-based image retrieval with spatial and temporal constructs
TLDR
A knowledge-based approach to retrieve medical images by feature and content with spatial and temporal constructs is developed and the KMeD (Knowledge-based Medical Database) system is implemented using these concepts.Abstract:
A knowledge-based approach to retrieve medical images by feature and content with spatial and temporal constructs is developed. Selected objects of interest in an image are segmented and contours are generated. Features and content are extracted and stored in a database. Knowledge about image features can be expressed as a type abstraction hierarchy (TAH), the high-level nodes of which represent the most general concepts. Traversing TAH nodes allows approximate matching by feature and content if an exact match is not available. TAHs can be generated automatically by clustering algorithms based on feature values in the databases and hence are scalable to large collections of image features. Since TAHs are generated based on user classes and applications, they are context- and user-sensitive. A knowledge-based semantic image model is proposed to represent the various aspects of an image object's characteristics. The model provides a mechanism for accessing and processing spatial, evolutionary and temporal queries. A knowledge-based spatial temporal query language (KSTL) has been developed that extends ODMG's OQL and supports approximate matching of features and content, conceptual terms and temporal logic predicates. Further, a visual query language has been developed that accepts point-click-and-drag visual iconic input on the screen that is then translated into KSTL. User models are introduced to provide default parameter values for specifying query conditions. We have implemented the KMeD (Knowledge-based Medical Database) system using these concepts.read more
Citations
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Journal ArticleDOI
ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases
TL;DR: A human-in-the-loop approach in which the human delineates the pathology bearing regions (PBR) and a set of anatomical landmarks in the image when the image is entered into the database is implemented.
Journal ArticleDOI
Content-based image retrieval in medical applications
Thomas Martin Lehmann,Mark Oliver Güld,Christian Thies,Benedikt Fischer,Klaus Spitzer,Daniel Keysers,Hermann Ney,Michael Kohnen,Henning Schubert,Berthold B. Wein +9 more
TL;DR: The proposed architecture is suitable for content-based image retrieval in medical applications and improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.
Journal ArticleDOI
Automatic categorization of medical images for content-based retrieval and data mining.
Thomas Martin Lehmann,Mark Oliver Güld,Thomas Deselaers,Daniel Keysers,Henning Schubert,Klaus Spitzer,Hermann Ney,Berthold B. Wein +7 more
TL;DR: This paper evaluates automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined, which is sufficient for medical CBIR applications.
Journal ArticleDOI
Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data.
Ashnil Kumar,Jinman Kim,Weidong Cai,Michael J. Fulham,Michael J. Fulham,Dagan Feng,Dagan Feng +6 more
TL;DR: This paper presents a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieved, and retrieval from diverse datasets.
Journal ArticleDOI
Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework
Hayit Greenspan,A.T. Pinhas +1 more
TL;DR: The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (GMM) along with information-theoretic image matching via the Kullback-Leibler (KL) measure and results are presented for comparing images to learned category models.
References
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Maintaining knowledge about temporal intervals
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Proceedings ArticleDOI
QBIC project: querying images by content, using color, texture, and shape
Carlton Wayne Niblack,R. Barber,Will Equitz,Myron D. Flickner,Eduardo H. Glasman,Dragutin Petkovic,Peter Cornelius Yanker,Christos Faloutsos,Gabriel Taubin +8 more
TL;DR: The main algorithms for color texture, shape and sketch query that are presented, show example query results, and discuss future directions are presented.