Proceedings of the international workshop on Workshop on multimedia information retrieval
Semantic content analysis and annotation of histological images
Computers in Biology and Medicine
Application of the synergetic algorithm on the classification of lymph tissue cells
Computers in Biology and Medicine
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Prototype System for Semantic Retrieval of Neurological PET Images
Medical Imaging and Informatics
A pattern similarity scheme for medical image retrieval
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
IEEE Transactions on Image Processing
Shape-based tumor retrieval in mammograms using relevance-feedback techniques
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Medical image retrieval, indexing and enhancement techniques: a survey
Proceedings of the 2011 International Conference on Communication, Computing & Security
Multiple 3D Medical Data Watermarking for Healthcare Data Management
Journal of Medical Systems
An efficient content based image retrieval framework using machine learning techniques
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
An endmember-based distance for content based hyperspectral image retrieval
Pattern Recognition
Region-based image retrieval using the semantic cluster matrix and adaptive learning
International Journal of Computational Science and Engineering
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A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework