Abstract
This paper proposes a new descriptor for radiological image retrieval. The proposed approach is based on fuzzy shape contexts, Fourier transforms and Eigenshapes. First, fuzzy shape context histograms are computed. Then, a 2D FFT is performed on each 2D histogram to achieve rotation invariance. Finally, histograms are projected onto a lower dimensionality feature space whose basis is formed by a set of vectors called Eigenshapes. They highlight the most important variations between shapes. The proposed approach is translation, scale and rotation invariant. Classes of the medical IRMA database are used for experiments. Comparison with the known approach rotation invariant shape contexts based on feature-space Fourier transformation proves that the proposed method is faster, more efficient, and robust to local deformations.
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Akgl, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acarl, B.: Content-based image retrieval in radiology: current status aExperimentsnd future directions. Digit. Imaging 24, 208–222 (2011)
Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of Content-based image retrieval systems in medical applications-clinical benefits and future directions. International Journal of Medical Informatics 73(1) (2004)
Šajn, L., Kukar, M.: Image processing and machine learning for fully automated probabilistic evaluation of medical images. Computer Methods and Programs in Biomedicine 104(3), 75–86 (2011)
Krefting, D., Vossberg, M., Hoheisel, A., Tolxdorff, T.: Simplified implementation of medical image processing algorithms into a grid using a workflow management system. Future Generation Computer Systems 26(4), 681–684 (2010)
Mahmoudi, S.E., Akhondi-Asl, A., Rahmani, R., Faghih-Roohi, S., Taimouri, V., Sabouri, A., Soltanian-Zadeh, H.: Web-based interactive 2D/3D medical image processing and visualization software. Computer Methods and Programs in Biomedicine 98(2), 172–182 (2010)
Martínez, A., Jiménez, J.J.: Tracking by means of geodesic region models applied to multidimensional and complex medical images. Computer Vision and Image Understanding 115(8), 1083–1098 (2011)
Wei, L., Yang, Y., Nishikawa, R.M.: Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis. Pattern Recognition 42(6), 1126–1132 (2009)
Chen, D.R., Huang, Y.L., Lin, S.H.: Computer-aided diagnosis with textural features for breast lesions in sonograms. Computerized Medical Imaging and Graphics 35(3), 220–226 (2011)
Kuo, W.J., Chang, R.F., Lee, C.C., Moon, W.K., Chen, D.R.: Retrieval technique for the diagnosis of solid breast tumors on sonogram. Ultrasound in Medicine and Biology 28(7), 903–909 (2002)
Bottigli, U., Golosio, B.: Feature extraction from mammographic images using fast marching methods. Nuclear Instruments and Methods in Physics 487(1-2), 209–215 (2002)
Yang, S., Wang, Y.: Rotation invariant shape contexts based on feature-space Fourier transformation. In: Fourth International Conference on Image and Graphics (2007)
Belongie, S., Malik, J.: Matching with Shape Contexts. In: IEEE on Content based Access of Image and Video Libraries, CBAIVL 2000 (2000)
Belongie, S., Malik, J., Puzicha, J.: Shape Context: A new descriptor for shape matching and object recognition (2001)
Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(4) (2002)
Diplaros, A., Gevers, T., Patras, I.: Color-Shape Context for Object Recognition. In: IEEE Workshop on Color and Photometric Methods in Computer Vision, in Conjunction with the 9th Int. Conf. Computer Vision (2003)
Kortgen, M., Park, G.-J., Novotni, M., Klein, R.: 3D Shape Matching with 3D Shape Contexts
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Ayed, A.B., Kardouchi, M., Selouani, SA. (2012). Rotation Invariant Fuzzy Shape Contexts Based on Eigenshapes and Fourier Transforms for Efficient Radiological Image Retrieval. In: Elmoataz, A., Mammass, D., Lezoray, O., Nouboud, F., Aboutajdine, D. (eds) Image and Signal Processing. ICISP 2012. Lecture Notes in Computer Science, vol 7340. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31254-0_49
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DOI: https://doi.org/10.1007/978-3-642-31254-0_49
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