Applied multivariate statistical analysis
Applied multivariate statistical analysis
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
IBM Journal of Research and Development
C4.5: programs for machine learning
C4.5: programs for machine learning
Supporting similarity queries in MARS
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
NeTra: a toolbox for navigating large image databases
Multimedia Systems - Special issue on video content based retrieval
ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Image classification and querying using composite region templates
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Efficient Matching and Indexing of Graph Models in Content-Based Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer and Robot Vision
Digital Picture Processing
A Knowledge-Based Approach for Retrieving Images by Content
IEEE Transactions on Knowledge and Data Engineering
Knowledge-Based Image Retrieval with Spatial and Temporal Constructs
IEEE Transactions on Knowledge and Data Engineering
Fast and Effective Retrieval of Medical Tumor Shapes
IEEE Transactions on Knowledge and Data Engineering
Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
ImageRover: A Content-Based Image Browser for the World Wide Web
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Local versus Global Features for Content-Based Image Retrieval
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Bimodal System for Interactive Indexing and Retrieval of Pathology Images
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Content-based retrieval of dynamic PET functional images
IEEE Transactions on Information Technology in Biomedicine
Content-based image database system for epilepsy
Computer Methods and Programs in Biomedicine
EBS k-d tree: an entropy balanced statistical k-d tree for image databases with ground-truth labels
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Bi-modal conceptual indexing for medical image retrieval
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Computer-aided diagnosis of radiographic patterns of lung disease via MDCT images
International Journal of Computational Science and Engineering
VisMed: a visual vocabulary approach for medical image indexing and retrieval
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Inter-media concept-based medical image indexing and retrieval With UMLS at IPAL
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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It is often difficult to come up with a well-principled approach to the selection of low-level features for characterizing images for content-based retrieval. This is particularly true for medical imagery, where gross characterizations on the basis of color and other global properties do not work. An alternative for medical imagery consists of the "scattershot" approach that first extracts a large number of features from an image and then reduces the dimensionality of the feature space by applying a feature selection algorithm such as the Sequential Forward Selection method.This contribution presents a better alternative to initial feature extraction for medical imagery. The proposed new approach consists of (i) eliciting from the domain experts (physicians, in our case) the perceptual categories they use to recognize diseases in images; (ii) applying a suite of operators to the images to detect the presence or the absence of these perceptual categories; (iii) ascertaining the discriminatory power of the perceptual categories through statistical testing; and, finally, (iv) devising a retrieval algorithm using the perceptual categories. In this paper we will present our proposed approach for the domain of high-resolution computed tomography (HRCT) images of the lung. Our empirical evaluation shows that feature extraction based on physicians' perceptual categories achieves significantly higher retrieval precision than the traditional scattershot approach. Moreover, the use of perceptually based features gives the system the ability to provide an explanation for its retrieval decisions, thereby instilling more confidence in its users.