A Study of Gaps in CBMIR Using Different Methods and Prospective

Authors: Pradeep Singh, Sukhwinder Singh, Gurjinder Kaur

Abstract:

In recent years, rapid advances in software and hardware in the field of information technology along with a digital imaging revolution in the medical domain facilitate the generation and storage of large collections of images by hospitals and clinics. To search these large image collections effectively and efficiently poses significant technical challenges, and it raises the necessity of constructing intelligent retrieval systems. Content-based Image Retrieval (CBIR) consists of retrieving the most visually similar images to a given query image from a database of images[5]. Medical CBIR (content-based image retrieval) applications pose unique challenges but at the same time offer many new opportunities. On one hand, while one can easily understand news or sports videos, a medical image is often completely incomprehensible to untrained eyes.

Keywords: Classification, clustering, content-based image retrieval (CBIR), relevance feedback (RF), statistical similarity matching, support vector machine (SVM).

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057959

References:

[1] Mustafa O, Ediz P. A color image segmentation approach for contentbased image retrieval. Pattern Recognition, 2007.40(4):1318-1325
[2] Haim P, Joseph F, Ian J. A study of Gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recognition, 2006.39(4):695-706
[3] Li, W, You J, Zhang D. Texture-based palm print retrieval using a layered search scheme for personal identification. IEEE Transcations on Multimedia, 2005.7(5), 891-898
[4] A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Machine Intell., 22(12):1349-1380, December 2000.
[5] D. G. Brown. The evaluation of computer-aided diagnosis systems: An FDA perspective. In 30th Applied Imagery Pattern Recognition Workshop, 2001.
[6] A. Smeulder, M. Worring, S. Santini, A. Gupta, and R. Jain, "Contentbased image retrieval at the end of the early years," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349-1380, Dec. 2003.
[7] A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Machine Intell., 22(12):1349-1380, December 2000.
[8] H. M├╝ller, N. Michoux, D. Bandon, and A. Geissbuhler. A review of content-based image retrieval systems in medical applications - clinical benefits and future directions. Int-l J. of Medical Informatics, 73(1):1- 23, 2004.
[9] H. M├╝ller, A. Rosset, A. Garcia, J.-P. Vallée, and A. Geissbuhler. Informatics in radiology (inforad): Benefits of content-based visual data access in radiology. RadioGraphics, 19:33-54, 2005.
[10] P. Buitelaar, M. Sintek, and M. Kiesel. A lexicon model for multilingual/multimedia ontologies. Proc. 3rd EuropeanSemantic Web Conference (ESWC06), June 2006.
[11] M. Romanelli, P. Buitelaar, and M. Sintek. Modeling linguistic facets of multimedia content for semantic annotation. In Proc. Int-l Conf. Semantics & digital Media Tech., December 2007.
[12] W. Hong, B. Georgescu, X. S. Zhou, S. Krishnan, Y. Ma, and D. Comaniciu. Database-guided simultaneous multi-slice 3D segmentation for volumetric data. In European Conf. Computer Vision, volume 3954, pages 397-409, May 2006.
[13] A. Jerebko, G. Schmidt, X. Zhou, J. Bi, V. Anand, J. Liu, S. Schoenberg, I. Schmuecking, B. Kiefer, and A. Krishnan. Computer-aided detection of skeletal metastases in MRI STIR imaging of the spine. In Proc. Info. Processing in Medical Imaging (IPMI), 2007.
[14] Z. Tu, X. S. Zhou, L. Bogoni, A. Barbu, and D. Comaniciu.Probabilistic 3D polyp detection in CT images: The role of sample alignment. IEEE CVPR, 2:1544-1551, May 2006.
[15] M. Sermesant, C. Forest, X. Pennec, H. Delingette, and N. Ayache. Deformable biomechanical models: Application to 4D cardiac image analysis. Med. Image Anal, 7, 2003.
[16] X. S. Zhou, D. Comaniciu, and A. Gupta. An information fusion framework for robust shape tracking. IEEE Trans.Pattern Anal. Machine Intell., 27(1):115-129, 2005
[17] T. Deselaers, D. Keysers, and H. Ney. FIRE - flexible image retrieval engine: ImageCLEF 2004 evaluation. In CLEF 2004, LNCS 3491, pages 688-698, September 2004..
[18] M. M. Rahman, B. C. Desai, and P. Bhattacharya, "Medical Image Retrieval with Probabilistic Multi-Class Support Vector Machine Classifiers and Adaptive Similarity Fusion," Computerized Medical Imaging and Graphics, (Publisher: Elsevier). Accepted for publication.2007.
[19] Rahman MM, Sood V, Desai BC, Bhattacharya P. CINDI at Image CLEF 2006: image retrieval & annotation tasks for the general photographic and medical image collections. In: Evaluation of multilingual and multi-modal information retrievalÔÇöseventh workshop of the cross-language evaluation forum (CLEF 2006); 2007. Proc LNCS 2006; 4730:715-24.
[20] M. M. Rahman, Bipin C. Desai, Prabir Bhattacharya, "Multi-Modal Interactive Approach to Image CLEF 2007 Photographic and Medical Retrieval Tasks by CINDI," Working Notes of the 2007 CLEF Workshop, Sep., 2007, Budapest, Hungary.,
[21] A. Blum and T. Mitchell, "Combining labeled and unlabeled data with cotraining," COLT: Proceedings of the Workshop on Computational Learning Theory.
[22] T. K. Ho, "The random subspace method for constructing decisionforests," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 8, pp.832-844, Aug. 1998.
[23] M. M. Rahman, Varun Sood, Bipin C. Desai, Prabir Bhattacharya, "Cross-Modal Interaction and Integration with Relevance Feedback for Medical Image Retrieval ," 13th International Multimedia Modeling Conference (MMM 2007), Singapore, Jan 9-12, 2007, Proceedings of LNCS,
[24] Feature for image retrieval: an experimental comparison: Thomas Deselaere. Keysers. Ney Dec 2007 Springer Science Media 2007
[25] M. M. Rahman, P. Bhattacharya and B. C. Desai, "A Framework for Medical Image Retrieval using Machine Learning & Statistical Similarity Matching Techniques with Relevance Feedback," IEEE Trans. On Information Technology In Biomedicine, (Special Issue on Image Management in Healthcare Enterprises), vol. 11, no. 1, pp. 59-69, 2007.
[26] H.D. Tagare, C. Jaffe, J. Duncan, Medical image databases: a contentbased retrieval approach, J. Am. Med. Informatics Assoc. 4 (3) (1997) 184ÔÇö198.
[27] B. Kaplan, H.P. Lundsgaarde, Toward an evaluation of an integrated clinical imaging system: Identifying clinical benefits, Methods Inform. Med. 35 (1996) 221ÔÇö229.
[28] T. Lehmann, M. G├╝ld, C. Thies, B. Fischer, K. Spitzer, D. Keysers, H. Ney, M. Kohnen, H. Schubert, and B. Wein.Content-based image retrieval in medical applications. Methods Inf. Med., 43, 2004.
[29] J. Vompras. Towards adaptive ontology-based image retrieval. In 17th GI-Workshop on the Foundations of Databases, Wörlitz, Germany, pages 148-152, May 2005.
[30] G. T. Papadopoulosa, V. Mezaris, S. Dasiopoulou, and I. Kompatsiaris. Semantic image analysis using a learning approach and spatial context. In Proc. 1st Int-l Conf.Semantics & digital Media Tech., December 2006.
[31] L. Su, B. Sharp, and C. Chibelushi. Knowledge-based image understanding: A rule-based production system for X-ray segmentation. In Proc. Int-l Conf. Enterprise Info. System, volume 1, pages 530-533, Spain, April 2002.
[32] A. Mechouche, C. Golbreich, and B. Gibaud. Towards an hybrid system using an ontology enriched by rules for the semantic annotation of brain MRI images. In Lecture Notes Computer Sci., volume 4524, pages 219- 228, June 2007.
[33] S. Patwardhan, A. Dhawan, and P. Relue. Classification of melanoma using tree structured wavelet transforms. Computer Methods and Programs in Biomedicine, 72(3):223-239, 2003.
[34] P. Schmidt-Saugeon, J. Guillod, and J.-P. Thiran. Towards a computeraided diagnosis system for pigmented skin lesions.Computerized Med. Imaging & Graphics, 27:65-78, 2003.
[35] X. S. Zhou, Y. Rui, and T. S. Huang. Exploration of Visual Data. Kluwer Academic Publishers, 2003.