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Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method...
With the increasing amount of image data available for cancer staging and diagnosis, it is clear that content-based image retrieval techniques are becoming more important to assist physicians in making diagnoses and tracking disease. Domain-specific feature descriptors have been previously shown to be effective in the retrieval of lung tumors. This work proposes a method to improve the rotation invariance...
Multimodal medical data from various information sources are often used to depict patients. We refer to each source as a ‘view’. Multi-view features could provide complementary information to each other; thus by fusing the multi-view features, we could greatly enhance the current medical content-based retrieval framework. In this paper, we propose a Co-neighbor Multi-view Spectral Embedding (CMSE)...
Neuroimaging is a fundamental component of the neurological diagnosis. The greatly increased volume and complexity of neuroimaging datasets has created a need for efficient image management and retrieval. In this paper, we advance a content-based retrieval framework for 3D functional neuroimaging data based on 3D curvelet transforms. The localized volumetric texture feature was extracted by a 3D digital...
The increased volume of 3D neuroimaging data has created a need for efficient data management and retrieval. We suggest that image retrieval via robust volumetric features could benefit managing these large image datasets. In this paper, we introduce a new feature extraction method, based on disorder-oriented masks, that uses the volumetric spatial distribution patterns in 3D physiological parametric...
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