Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based retrieval of dynamic PET functional images
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
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Positron Emission Tomography (PET) is used within neurology to study the underlying biochemical basis of cognitive functioning. Due to the inherent lack of anatomical information its study in conjunction with image retrieval is limited. Content based image retrieval (CBIR) relies on visual features to quantify and classify images with a degree of domain specific saliency. Numerous CBIR systems have been developed semantic retrieval, has however not been performed. This paper gives a detailed account of the framework of visual features and semantic information utilized within a prototype image retrieval system, for PET neurological data. Images from patients diagnosed with different and known forms of Dementia are studied and compared to controls. Image characteristics with medical saliency are isolated in a top down manner, from the needs of the clinician - to the explicit visual content. These features are represented via Gabor wavelets and mean activity levels of specific anatomical regions. Preliminary results demonstrate that these representations are effective in reflecting image characteristics and subject diagnosis; consequently they are efficient indices within a semantic retrieval system.