Integrating an automatic classification method into the medical image retrieval process

Combining low-level features that represent the content of medical images with high level features that are saved with images would allow the expansion of text queries submitted to Content Based Image Retrieval (CBIR) systems. Expanding these text queries would allow CBIR systems to respond more effectively to specific queries when retrieving medical images. We hypothesized that adding an automatic classification method to the current retrieval process would help improve the performance of the University at Buffalo Medical Text and Images Retrieval System (UBMedTIRS). This paper illustrates the results of our approach and its implications for expanding query statements in medical image information retrieval (IR) systems.

[1]  Hayit Greenspan,et al.  Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework , 2007, IEEE Transactions on Information Technology in Biomedicine.

[2]  Michael Kohnen,et al.  The IRMA code for unique classification of medical images , 2003, SPIE Medical Imaging.

[3]  Thomas Martin Deserno,et al.  Evaluation axes for medical image retrieval systems: the imageCLEF experience , 2005, ACM Multimedia.

[4]  Miguel E. Ruiz,et al.  Combining Image Features, Case Descriptions and UMLS Concepts to Improve Retrieval of Medical Images , 2006, AMIA.

[5]  Thomas Martin Deserno,et al.  Content-based image retrieval in medical applications: a novel multistep approach , 1999, Electronic Imaging.

[6]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[7]  Joo-Hwee Lim,et al.  Medical-Image Retrieval Based on Knowledge-Assisted Text and Image Indexing , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Lois F. Lunin.  Imaging Systems: What, When, How. , 1992 .

[9]  Hermann Ney,et al.  FIRE in ImageCLEF 2005: Combining Content-based Image Retrieval with Textual Information Retrieval , 2005, CLEF.

[10]  Steven C. Horii,et al.  Review: Understanding and Using DICOM, the Data Interchange Standard for Biomedical Imaging , 1997, J. Am. Medical Informatics Assoc..

[11]  Thierry Pun,et al.  Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis , 2004, International Journal of Computer Vision.

[12]  P. Tang,et al.  Medical Computer Applications in Health Care and Biomedicine , 2002 .

[13]  Ben J Hicks,et al.  SPIE - The International Society for Optical Engineering , 2001 .

[14]  Thomas M. Lehmanna,et al.  Header for SPIE use Content-Based Image Retrieval in Medical Applications: A Novel Multi-Step Approach , 2022 .