A method for linking computed image features to histological semantics in neuropathology
Journal of Biomedical Informatics
Histopathology Image Classification Using Bag of Features and Kernel Functions
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Content-based image database system for epilepsy
Computer Methods and Programs in Biomedicine
A pattern similarity scheme for medical image retrieval
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Brain CT image database building for computer-aided diagnosis using content-based image retrieval
Information Processing and Management: an International Journal
Content-based histopathology image retrieval using a kernel-based semantic annotation framework
Journal of Biomedical Informatics
Histology image indexing using a non-negative semantic embedding
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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A prototype, content-based image retrieval system has been built employing a client/server architecture to access supercomputing power from the physician's desktop. The system retrieves images and their associated annotations from a networked microscopic pathology image database based on content similarity to user supplied query images. Similarity is evaluated based on four image feature types: color histogram, image texture, Fourier coefficients, and wavelet coefficients, using the vector dot product as a distance metric. Current retrieval accuracy varies across pathological categories depending on the number of available training samples and the effectiveness of the feature set. The distance measure of the search algorithm was validated by agglomerative cluster analysis in light of the medical domain knowledge. Results show a correlation between pathological significance and the image document distance value generated by the computer algorithm. This correlation agrees with observed visual similarity. This validation method has an advantage over traditional statistical evaluation methods when sample size is small and where domain knowledge is important. A multi-dimensional scaling analysis shows a low dimensionality nature of the embedded space for the current test set.