A Comparative Study of Similarity Measures for Content-Based Medical Image Retrieval

This note summarizes methodologies employed in our submissions for the medical retrieval subtask of 2012 ImageCLEF competition. Our work aims to provide a systematic comparison of various similarity measures in the Medical CBIR application context. Our system consists of the standard bag-of-words features with SIFT. Computed features are then compared by using various plug-in similarity measures, including diffusion distance and information-theoretic metric learning. This note provides the results of our experimental validation using the 2011 ImageCLEF dataset.

[1]  Mark Sanderson,et al.  Seven Years of Image Retrieval Evaluation , 2010, ImageCLEF.

[2]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[3]  Joachim M. Buhmann,et al.  Non-parametric similarity measures for unsupervised texture segmentation and image retrieval , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[5]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[6]  Gert R. G. Lanckriet,et al.  Metric Learning to Rank , 2010, ICML.

[7]  Joachim M. Buhmann,et al.  Empirical evaluation of dissimilarity measures for color and texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Carla E. Brodley,et al.  CBIR for medical images - an evaluation trial , 2001, Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL 2001).

[9]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[11]  Henning Müller,et al.  Overview of the ImageCLEF 2012 Medical Image Retrieval and Classification Tasks , 2012, CLEF.

[12]  Marina Bosch,et al.  ImageCLEF, Experimental Evaluation in Visual Information Retrieval , 2010 .

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

[14]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[15]  José Luis Vicedo González,et al.  TREC: Experiment and evaluation in information retrieval , 2007, J. Assoc. Inf. Sci. Technol..

[16]  Donald Geman,et al.  Boundary Detection by Constrained Optimization , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Rong Jin,et al.  Distance Metric Learning: A Comprehensive Survey , 2006 .

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

[19]  Michael Werman,et al.  The Quadratic-Chi Histogram Distance Family , 2010, ECCV.

[20]  Thomas Martin Deserno,et al.  Ontology of Gaps in Content-Based Image Retrieval , 2009, Journal of Digital Imaging.

[21]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[22]  Henning Müller,et al.  The Medical Image Retrieval Task , 2010, ImageCLEF.

[23]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[24]  Hayit Greenspan,et al.  Medical Image Classification at Tel Aviv and Bar Ilan Universities , 2010, ImageCLEF.

[25]  Haibin Ling,et al.  Diffusion Distance for Histogram Comparison , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[26]  Peter J. Bickel,et al.  The Earth Mover's distance is the Mallows distance: some insights from statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[27]  Haibin Ling,et al.  An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  William R. Hersh,et al.  Relevance Judgments for Image Retrieval Evaluation , 2010, ImageCLEF.

[29]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[30]  Michael I. Jordan,et al.  Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.

[31]  Tongyuan Wang,et al.  Medical image retrieval and registration: towards computer assisted diagnostic approach , 2004, 2004 IDEAS Workshop on Medical Information Systems: The Digital Hospital (IDEAS-DH'04).