Automatic Multilevel Medical Image Annotation and Retrieval

Image retrieval at the semantic level mostly depends on image annotation or image classification. Image annotation performance largely depends on three issues: (1) automatic image feature extraction; (2) a semantic image concept modeling; (3) algorithm for semantic image annotation. To address first issue, multilevel features are extracted to construct the feature vector, which represents the contents of the image. To address second issue, domain-dependent concept hierarchy is constructed for interpretation of image semantic concepts. To address third issue, automatic multilevel code generation is proposed for image classification and multilevel image annotation. We make use of the existing image annotation to address second and third issues. Our experiments on a specific domain of X-ray images have given encouraging results.

[1]  Alexander J. Smola,et al.  Advances in Large Margin Classifiers , 2000 .

[2]  Thomas Serre,et al.  Hierarchical classification and feature reduction for fast face detection with support vector machines , 2003, Pattern Recognit..

[3]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  James Ze Wang,et al.  Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  A. Hanbury.  Review of Image Annotation for the Evaluation of Computer Vision Algorithms , 2006 .

[6]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[7]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[8]  David A. Forsyth,et al.  Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary , 2002, ECCV.

[9]  Hermann Ney,et al.  Classification of Medical Images using Non-linear Distortion Models , 2004, Bildverarbeitung für die Medizin.

[10]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[11]  John R. Smith,et al.  Semantic Indexing of Multimedia Content Using Visual, Audio, and Text Cues , 2003, EURASIP J. Adv. Signal Process..

[12]  A. Mueen M. Sapiyan Baba and R. Zainuddin.  Multilevel Feature Extraction and X-ray Image Classification , 2007 .

[13]  Gustavo Carneiro,et al.  Formulating semantic image annotation as a supervised learning problem , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[15]  R. Manmatha,et al.  A Model for Learning the Semantics of Pictures , 2003, NIPS.

[16]  Thomas Serre,et al.  Hierarchical Classification and Feature Reduction for Fast Face Detection , 2005 .