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
Variation of relevance assessments for medical image retrieval
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
Diffuse parenchymal lung diseases: 3D automated detection in MDCT
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Artificial Intelligence in Medicine
Isotropic polyharmonic B-splines: scaling functions and wavelets
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Texture bags: anomaly retrieval in medical images based on local 3d-texture similarity
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
VISCERAL: towards large data in medical imaging -- challenges and directions
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Medical (visual) information retrieval
PROMISE'12 Proceedings of the 2012 international conference on Information Retrieval Meets Information Visualization
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In this paper, a computer–aided diagnosis (CAD) system that retrieves similar cases affected with an interstitial lung disease (ILDs) to assist the radiologist in the diagnosis workup is presented and evaluated. The multimodal inter–case distance measure is based on a set of clinical parameters as well as automatically segmented 3–dimensional regions of lung tissue in high–resolution computed tomography (HRCT) of the chest. A global accuracy of 75.1% of correct matching among five classes of lung tissues as well as a mean average retrieval precision at rank 1 of 71% show that automated lung tissue categorization in HRCT data is complementary to case–based retrieval both from the user's viewpoint and also on the algorithmic side.