Academic Radiology
Volume 16, Issue 10 , Pages 1171-1178 , October 2009

Assessment of Performance Improvement in Content-based Medical Image Retrieval Schemes Using Fractal Dimension

Received 9 December 2008 ,Accepted 21 April 2009.

References 

  1. Muller H, Michoux N, Bandon D, Geissbuhler A. A review of content-based image retrieval systems in medical applications—clinical benefit and future direction. Int J Med Inform. 2004;73:1–23
  2. Lehmann TM, Guld MO, Deselaers T, et al. Automatic categorization of medical images for content-based retrieval and data mining. Comput Med Imaging Graph. 2005;29:143–155
  3. El-Naqa I, Yang Y, Galatsanos NP, Wernick M. A similarity learning approach to content based image retrieval: application to digital mammography. IEEE Trans Med Imaging. 2004;23:1233–1244
  4. Lam MO, Disney T, Raicu DS, Furst J, Channin DS. BRISC—an open source pulmonary nodule image retrieval framework. J Digit Imaging. 2007;20:63–77
  5. Smeulders AMW, Worring M, Santini S, Gupta A, Jain R. Content-based image retrieval at the end of the early years. IEEE Trans Patt Anal Mach Intell. 2000;22:1349–1380
  6. Park SC, Sukthankar R, Mummert L, Satyanarayanan M, Zheng B. Optimization of reference library used in content-based medical image retrieval scheme. Med Phys. 2007;34:4331–4339
  7. Muramatsu C, Li Q, Suzuki K, et al. Investigation of psychophysical measures for evaluation of similar images for mammographic masses: preliminary results. Med Phys. 2005;32:2295–2304
  8. Karam OH, Hamad AM, Ghoniemy S, Rady S. Enhancement of wavelet-based medical image retrieval through feature evaluation using an information gain measure. In: Proceedings of the Eighteenth Annual ACM Symposium on Applied Computing. New York: Association for Computing Machinery; 2003;p. 9–12
  9. Alto H, Rangayyan RM, Desautels JEL. Content-based retrieval and analysis of mammographic masses. J. Electron Imaging. 2005;14:1–17
  10. Wei C, Li C, Wilson R. A general framework for content-based medical image retrieval with its application to mammograms. Proc SPIE. 2005;5748:134–143
  11. Muramatsu C, Li Q, Schmidt RA, et al. Determination of subjective similarity for pairs of masses and pairs of clustered microcalcifications on mammograms: comparison of similarity ranking scores and absolute similarity ratings. Med Phys. 2007;34:2890–2895
  12. Zheng B, Lu A, Hardesty LA, Gur D. A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment. Med Phys. 2006;33:111–117
  13. Wang XH, Park SC, Zheng B. Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment. Phys Med Biol. 2009;54:949–961
  14. Lefebvre F, Benali H, Gilles R, Kahn E, Paola RD. A Fractal approach to the segmentation of microcalcifications in digital mammograms. Med Phys. 1995;22:381–390
  15. Sedivy R, Windischberger C, Svozil K, Moser E, Breitenecker G. Fractal analysis: an objective method for identifying atypical nuclei in dysplastic lesions of the cervix uteri. Gynecol Oncol. 1999;75:78–83
  16. Geraets WG, van der Stelt PF. Fractal properties of bone. Dentomaxillofac Radiol. 2000;29:144–153
  17. Li H, Giger ML, Olopade OI, Lanm L. Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. Acad Radiol. 2007;4:513–521
  18. Hendee WR. Cognitive interpretation of visual signals. In:  Hendee WR,  Wells PNT editor. The perception of visual information. 2nd ed. New York: Springer-Verlag; 1997;p. 149–176
  19. Rangayyan RM, Nguyen TM. Fractal analysis of contours of breast masses in mammograms. J Digit Imaging. 2007;20:223–237
  20. Xu C, Zhuang TG, Hua YQ. Discrimination of ground glass opacity on lung HRCT images using visual complexity measurements. In: Proceeding of the Second Joint EMBS/BMES Conference. New York: IEEE; 2002;p. 1116–1117
  21. Peng F, Yu X, Xu G, Xia Q. Fuzzy classification based on fractal features for undersea image. Int J Inform Technol. 2005;11:133–142
  22. Chevallet JP, Maillot N, Lim JH. Concept propagation based on visual similarity application to medical image annotation. Third Asia Inform Retriev Symp. 2006;4182:514–521
  23. Soares F, Andruszkiewic P, Freire MM, Cruz P, Pereira M. Self-similarity analysis applied to 2D breast cancer imaging. Proc Int Conf Syst Network Commun. 2007;1:1–6
  24. Velanovich V. Fractal analysis of mammographic lesions: a feasibility study quantifying the difference between benign and malignant masses. Am J Med Sci. 1996;311:211–214
  25. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. Automated analysis of mammographic densities. Phys Med Biol. 1996;41:909–923
  26. Chen J, Zheng B, Chang YH, Shaw CC, Towers JD, Gur D. Fractal analysis of trabecular patterns in projection radiographs. An assessment. Invest Radiol. 1994;29:624–629
  27. Millard J, Augat P, Link TM, et al. Power spectral analysis of vertebral trabecular bone structure from radiographs: orientation dependence and correlation with bone mineral density and mechanical properties. Calcifield Tissue Int. 1998;63:482–489
  28. Mitchell TM. Machine learning. Boston, MA: WCB/McGraw-Hill; 1997;
  29. Zheng B, Abrams G, Britton CA, et al. Evaluation of an interactive computer-aided diagnosis scheme for mammography: a pilot study. Proc SPIE. 2007;6515:65151M-1–65151M-8
  30. Felipe JC, Traina C, Traina AJ. A new family of distance functions for perceptual similarity retrieval of medical images. J Digit Imaging. 2009;22:183–201
  31. Zheng B, Abrams G, Leader JK, et al. Agreement between ratings of mass spiculations by observers and a computer scheme. Proc SPIE. 2007;6514:65141P-1–65141P-8

 This work is supported in part by grants 1 UL1 RR024153 from the National Center for Research Resources (Bethesda, MD) and CA101733 from the National Cancer Institute (Bethesda, MD) to the University of Pittsburgh.

PII: S1076-6332(09)00263-3

doi: 10.1016/j.acra.2009.04.009

Academic Radiology
Volume 16, Issue 10 , Pages 1171-1178 , October 2009