A simple multi-feature based stereoscopic medical image retrieval system

K.A. Shaheer Abubacker 1 , J. Sutha 2 , and K.A. Shahul Hameed 3
  • 1 Anna University, , Chennai, India
  • 2 Department of CSE, AAA College of Engineering & Technology, , Sivakasi, India
  • 3 Department of ECE, Sethu Institute of Technology, , Kariapatti, India

Abstract

This paper describes a method of retrieving stereoscopic medical images from the database that consists of feature extraction, similarity measure, and re-ranking of retrieved images. This method retrieves similar images of the query image from the database and re-ranks them according to the disparity map. The performance is evaluated using the metrics namely average retrieval precision (APR) and average retrieval rate (ARR). According to the performance outcomes, the multi-feature based image retrieval using Mahalanobis distance measure has produced better result compared to other distance measures namely Euclidean, Minkowski, the sum of absolute difference (SAD) and the sum of squared absolute difference (SSAD). Therefore, the stereo image retrieval systems presented has high potential in biomedical image storage and retrieval systems.

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  • [1] Getty DJ, D’Orsi CJ, Pickett RM. Stereoscopic digital mammography: Improved accuracy of lesion detection in breast cancer screening. In: Krupinski EA (ed) Digital Mammography. IWDM 2008. Lecture Notes in Computer Science, vol 5116. Springer, Berlin, Heidelberg. pp 74-79.

  • [2] Daul C, Graebling P, Tiedeu A, Wolf D. 3-D reconstruction of microcalcification clusters using stereo imaging: algorithm and mammographic unit calibration, IEEE Trans Biomed Eng. 2005;52(12):2058-2073.

  • [3] Niblack CW, Barber R, Equitz W, et al. The QBIC Project: Querying Images by Content, Using Color, Texture, and Shape. SPIE Conf on Storage and Retrieval for Image and Video Databases. 1993;1908.173-187.

  • [4] Welter P, Riesmeier J, Fischer B, et al. Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer aided diagnosis. J American Med Informatics Association. 2011;18(4):506-510.

  • [5] Lehmann TM, Wein B, Dahmen J, et al. Content-based Image Retrieval in Medical Applications: A Novel Multi-step Approach. Proces of SPIE - The Int Society for Optical Engineering. 2000;3972:312-320.

  • [6] Smeulders AW, Worring M, Santini S, et al. Content based image retrieval at the end of the early years. IEEE Trans on Pattern Analysis & Machine Intelligence. 2000;12:1349-1380.

  • [7] Karine A, El Maliani AD, El Hassouni M. A novel statistical model for content-based stereo image retrieval in the complex wavelet domain. J of Visual Comm and Image Representation. 2018;50:27-39.

  • [8] Cao Y, Kang K, Zhang S, et al. Automatic tag saliency ranking for stereo images. Neurocomputing. 2016;172:9-18.

  • [9] Chaker A, Kaaniche M, Benazza-Benyahia A. Disparity based stereo image retrieval through univariate and bivariate models. Signal Process. Image Comm. 2015;31:174-184.

  • [10] Gonzalez RC, Woods RE. Digital image processing, Prentice-Hall, Inc. Upper Saddle River, NJ, USA 2002.

  • [11] Huang J, Kumar SR, Mitra M, et al. Image indexing using color correlograms. Computer Vision and Pattern Recognition, 1997. Procs of 1997 IEEE Computer Society Conf on, IEEE. 1997;762-768.

  • [12] Shalul Hameed KA, Banumathi A, Ulaganathan G. Segmentation of immunohistochemical staining of β-catenin expression of oral cancer using gabor filter technique. In: Adv in Engg, Sci and Mgmt (ICAESM), 2012 Int Conf on, IEEE, 2012;429-434.

  • [13] Chen J, Shan S, He C, et al. WLD: A robust local image descriptor. IEEE transactions on pattern analysis and machine intelligence 2010;32(9):1705-1720.

  • [14] Feng Y, Ren J, Jiang J. Generic framework for content-based stereo image/video retrieval. IEEE Electronics letters. 2011;47(2):97-98.

  • [15] Zhang Q, Izquierdo E. Histology image retrieval in optimized multi-feature spaces. IEEE J of Biomedical and Health Informatics. 2013;17(1):240-249.

  • [16] Manjunath BS, Ma WY. Texture features for browsing and retrieval of image data. IEEE Trans on Pattern Analysis and Machine Intelligence. 1996;18(8):837–842.

  • [17] Verma M, Raman B. Local tri-directional patterns: A new texture feature descriptor for image retrieval. Digital Signal Processing. 2016;51:62-72.

  • [18] Murala S, Maheshwari R, Balasubramanian R. Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE transactions on image processing. 2012;21(5):2874-2886.

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