Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge
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We investigate the use of relevance feedback (RFb) and the inclusion of expert knowledge to reduce the semantic gap in content-based image retrieval (CBIR) of mammograms.
Materials and methods
Tests were conducted with radiologists, in which their judgment of the relevance of the retrieved images was used with techniques of query-point movement to incorporate RFb. The measures of similarity of images used for CBIR were based upon textural characteristics and the distribution of density of fibroglandular tissue in the breast. The features used include statistics of the gray-level histogram, texture features based upon the gray-level co-occurrence matrix, moment-based features, measures computed in the Radon domain, and granulometric measures. Queries for CBIR with RFb were executed by three radiologists. The performance of CBIR was measured in terms of precision of retrieval and a measure of relevance-weighted precision (RWP) of retrieval.
The results indicate improvement due to RFb of up to 62% in precision and 39% in RWP.
The gain in performance of CBIR with RFb depended upon the BI-RADS breast density index of the query mammographic image, with greater improvement in cases of mammograms with higher density.
KeywordsComputer-assisted radiographic image interpretation Computer-assisted image processing Automated pattern recognition Information retrieval
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- 6.Azevedo-Marques PM, Honda MH, Rodrigues JAH, Santos RR, Traina AJM, Traina Júnior C and Bueno JM (2002). Recuperação de Imagem Baseada em Conteúdo: Uso de Atributos de Textura para Caracterização de Microcalcificações Mamográficas. Rev Bras Radiol 35: 93–98 Google Scholar
- 8.Alto H, Rangayyan RM, Desautels JEL (2005) Content-based retrieval and analysis of mammographic masses. J Electron Imaging 14:023016:1–17Google Scholar
- 10.Tao EY, Sklansky J (1996) Analysis of mammograms aided by database of images of calcifications and textures. In: Medical imaging: image processing. Proceedings of SPIE, pp 988–995Google Scholar
- 11.Ornes CJ, Valentino DJ, Yoon H-J, Eisenman JI, Sklansky J (2001) Search engine for remote database-aided interpretation of digitized mammograms. In: Siegel El, Huang HK (eds) Medical imaging: PACS and integrated medical information systems: design and evaluation, pp 132–137Google Scholar
- 12.El-Naqa I, Yang Y, Galatsanos NP, Wernick MN (2002) Content-based image retrieval for digital mammography. In: Yongyi Y (eds) IEEE proceedings of the international conference on image processing, Rochester, pp 141–144Google Scholar
- 13.Nakagawa T, Hara T, Fujita H, Iwase T, Endo T (2002) Image retrieval system of mammographic masses by using local pattern matching technique. In: Peitgen H-O (ed) Digital mammography. Proceedings of the IWDM 2002 Bremen, Germany. Springer, New York, pp 562–565Google Scholar
- 14.Nakagawa T, Hara T, Fujita H, Iwase T, Endo T (2002) Development of a computer-aided sketch system for mammograms. In: Peitgen H-O (ed) Digital mammography. Proceedings of the IWDM 2002 Bremen, Germany. Springer, New York, pp 581–583Google Scholar
- 15.Baeza-Yates RA and Ribeiro-Neto BA (1999). Modern information retrieval. Addison-Wesley, Wokingham Google Scholar
- 16.Ortega-Binderberger M and Mehrotra S (2003). Relevance feedback in multimedia databases. In: Furht, B and Marques, O (eds) Handbook of video databases—design and applications, pp 511–536. CRC Press, Boca Raton Google Scholar
- 18.ACR (2003) BI-RADS® —mammography, 4th edn. American College of Radiology, RestonGoogle Scholar
- 20.Gonzalez RC and Woods RE (2007). Digital image processing. Prentice Hall, Englewood Cliffs Google Scholar
- 21.Ramm AG and Katsevich AI (1996). The radon transform and local tomography. CRC Press, Boca Raton Google Scholar
- 22.Hu M-K (1962). Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8: 179–187 Google Scholar
- 24.Rocchio JJ (1971). Relevance feedback in information retrieval. In: Cliffs, E (eds) The SMART retrieval system: experiments in automatic document processing, pp 313–323. Prentice Hall, Englewood Cliffs Google Scholar
- 25.Traina A, Marques J, Traina C (2006) Fighting the semantic gap on CBIR systems through new relevance feedback techniques. In: CBMS ’06: proceedings of the 19th IEEE international symposium on computer-based medical systems. IEEE Computer Society, Salt Lake City, pp 881–886Google Scholar