Annotation of Medical Images

[1]  D. Chen,et al.  Computer-aided diagnosis applied to US of solid breast nodules by using neural networks. , 1999, Radiology.

[2]  Yap-Peng Tan,et al.  A novel multi-scale spatial-color descriptor for content-based image retrieval , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[3]  Jake K. Aggarwal,et al.  Retrieval by classification of images containing large manmade objects using perceptual grouping , 2002, Pattern Recognit..

[4]  Betsy L. Humphreys,et al.  Relationships in Medical Subject Headings (MeSH) , 2001 .

[5]  Hamid Abrishami Moghaddam,et al.  A new algorithm for image indexing and retrieval using wavelet correlogram , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[6]  Joo-Hwee Lim,et al.  Home Photo Content Modeling for Personalized Event-Based Retrieval , 2003, IEEE Multim..

[7]  Shigeo Wada,et al.  Flexible color texture retrieval method using multi-resolution mosaic for image classification , 2002, 6th International Conference on Signal Processing, 2002..

[8]  Thomas Martin Deserno,et al.  Automatic medical image annotation in ImageCLEF 2007: Overview, results, and discussion , 2008, Pattern Recognit. Lett..

[9]  G. Tourassi.  Journey toward computer-aided diagnosis: role of image texture analysis. , 1999, Radiology.

[10]  J. Wyatt,et al.  eHealth and the future: promise or peril? , 2005, BMJ : British Medical Journal.

[11]  Henri A. Vrooman,et al.  Suitability of texture features to assess changes in trabecular bone architecture , 2002, Pattern Recognit. Lett..

[12]  Mingjing Li,et al.  Color texture moments for content-based image retrieval , 2002, Proceedings. International Conference on Image Processing.

[13]  Norimichi Tsumura,et al.  Why Multispectral Imaging In Medicine , 2004 .

[14]  P. Langenberg,et al.  Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment. , 2000, AJR. American journal of roentgenology.

[15]  M K Markey,et al.  The reliability of measuring physical characteristics of spiculated masses on mammography. , 2006, The British journal of radiology.

[16]  Sanjeev Khudanpur,et al.  Hidden Markov models for automatic annotation and content-based retrieval of images and video , 2005, SIGIR '05.

[17]  Mohan S. Kankanhalli,et al.  Pivot Vector Space Approach for Audio-Video Mixing , 2003, IEEE Multim..

[18]  N Karssemeijer,et al.  Use of border information in the classification of mammographic masses , 2006, Physics in medicine and biology.

[19]  Arnau Oliver,et al.  Comparison Between Wolfe, Boyd, BI-RADS and Tabár Based Mammographic Risk Assessment , 2006, Digital Mammography / IWDM.

[20]  Theo Gevers,et al.  Classifying color edges in video into shadow-geometry, highlight, or material transitions , 2003, IEEE Trans. Multim..

[21]  Anil K. Jain,et al.  Image classification for content-based indexing , 2001, IEEE Trans. Image Process..

[22]  Nikolas P. Galatsanos,et al.  A similarity learning approach to content-based image retrieval: application to digital mammography , 2004, IEEE Transactions on Medical Imaging.

[23]  Chang-Tsun Li,et al.  Calcification Descriptor and Relevance Feedback Learning Algorithms for Content-Based Mammogram Retrieval , 2006, Digital Mammography / IWDM.

[24]  Wan-Chi Siu,et al.  Multimedia Information Retrieval and Management , 2003 .

[25]  Chihli Hung and Chih-Fong Tsai,et al.  Automatically Annotating Images with Keywords: A Review of Image Annotation Systems , 2008 .

[26]  C. Floyd,et al.  Breast imaging reporting and data system standardized mammography lexicon: observer variability in lesion description. , 1996, AJR. American journal of roentgenology.

[27]  Kevin Ashley.  The preservation of databases , 2004 .

[28]  S. Majumdar,et al.  High-resolution magnetic resonance imaging: three-dimensional trabecular bone architecture and biomechanical properties. , 1998, Bone.