Medical Imaging and Informatics
X-Ray Image Classification and Retrieval Using Ensemble Combination of Visual Descriptors
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
A pattern similarity scheme for medical image retrieval
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Semantic feature extraction for brain CT image clustering using nonnegative matrix factorization
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Derivations of normalized mutual information in binary classifications
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Content based image retrieval from chest radiography databases
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Medical image retrieval, indexing and enhancement techniques: a survey
Proceedings of the 2011 International Conference on Communication, Computing & Security
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
MedFMI-SiR: a powerful DBMS solution for large-scale medical image retrieval
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
An endmember-based distance for content based hyperspectral image retrieval
Pattern Recognition
LS-SVM based image segmentation using color and texture information
Journal of Visual Communication and Image Representation
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Retrieval of high-dimensional visual data: current state, trends and challenges ahead
Multimedia Tools and Applications
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This paper presents an image representation and matching framework for image categorization in medical image archives. Categorization enables one to determine automatically, based on the image content, the examined body region and imaging modality. It is a basic step in content-based image retrieval (CBIR) systems, the goal of which is to augment text-based search with visual information analysis. CBIR systems are currently being integrated with picture archiving and communication systems for increasing the overall search capabilities and tools available to radiologists. 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. The GMM-KL framework is used for matching and categorizing X-ray images by body regions. A multidimensional feature space is used to represent the image input, including intensity, texture, and spatial information. Unsupervised clustering via the GMM is used to extract coherent regions in feature space that are then used in the matching process. A dominant characteristic of the radiological images is their poor contrast and large intensity variations. This presents a challenge to matching among the images, and is handled via an illumination-invariant representation. The GMM-KL framework is evaluated for image categorization and image retrieval on a dataset of 1500 radiological images. A classification rate of 97.5% was achieved. The classification results compare favorably with reported global and local representation schemes. Precision versus recall curves indicate a strong retrieval result as compared with other state-of-the-art retrieval techniques. Finally, category models are learned and results are presented for comparing images to learned category models