Content-based Medical Image Tetrieval for Liver CT Annotation

Authors

  • Imane Nedjar Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, Algeria
  • Saïd Mahmoudi Computer Science Department, Faculty of Engineering, University of Mons, Mons, Belgium
  • Mohammed Amine Chikh Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, Algeria

DOI:

https://doi.org/10.14738/tmlai.54.2985

Keywords:

Medical image, Liver annotation, Image Retrieval, BEMD, Gabor wavelet

Abstract

The increase number of medical image stored and saved every day presents a unique opportunity for content-based medical image retrieval (CBMIR) systems. In this paper, we propose content-based medical image retrieval for annotating liver CT scans images in order to generate a structured report. For that, we have used the Bidimentional Empirical Mode Decomposition (BEMD), and then we have applied Gabor wavelet transform to extract the mean and the standard deviation as features descriptors. Finally, a proposed similarity distance was employed to retrieve the most similar training images to the image query, and a majority voting scheme was used to select the answers for an unannotated image. We have used the IMAGECLEF 2015 annotation dataset and the obtained score was 88.9%.

References

(1) N.Marvasti et al., "ImageCLEF Liver CT Image Annotation Task 2014. In: CLEF 2014 Evaluation Labs and Workshop", Online Working Notes. (2014).

(2) I.Nedjar, S.Mahmoudi, A.Chikh, K.Abi-ayad, and Z.Bouafia, "Automatic annotation of liver CT image:ImageCLEFmed 2015," in CLEF2015 Working Note.CEUR Workshop Proceedings,CEUR-WS.org, Toulouse,france, Septembre 8-11 2015.

(3) A.Kumar, S.Dyer, C.Li, P.H.W.Leong, and J.Kim, "Automatic annotation of liver ct images: the submission of the bmet group to imageclefmed 2014", in CLEF 2014 Labs and Workshops, Notebook Papers. CEUR, Workshop Proceedings (CEUR-WS.org), September 2014.

(4) A.B.Spanier and L.Joskowicz, "Towards content-based image retrieval: From computer generated features to semantic descriptions of liver ct scans", in CLEF 2014 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings (CEUR-WS.org), September 2014.

(5) B.Ermis and A.T.Cemgil, "Liver ct annotation via generalized coupled tensor factorization", in CLEF 2014 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings (CEUR-WS.org), September 2014.

(6) N.Marvasti et al., "Overview of the ImageCLEF 2015 liver CT annotation task," in CLEF2015 Working Notes. Workshop Proceedings.CEUR-WS.org, no. 1613-0073, September 8-11. 2015.

(7) N.E.Huang et al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proc. R. Soc. London. A 454, 903-995, 1998.

(8) J.C.Nunes, Y.Bouaoune, E.Delechelle, O.Niang, and P.Bunel, "Image analysis by bidimensional empirical mode decomposition," Image and Vision Computing 21 1019–1026, 2003.

(9) C.Liu and H.Wechsler, "Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition," IEEE Transactions on Image Processing, vol. 11, no. 4, pp. 467-476, 2002.

(10) G.Zhang, ZM.Ma, and L.Deng, "Texture Feature Extraction and Description Using Fuzzy Set of Main Dominant Directions of Variable Scales in Content-Based Medical Image Retrieval," in SAC '08 Proceedings of the ACM symposium on Applied computing, Fortaleza, Ceará, Brazil, March 16-20, 2008, pp. 1760-1761.

Downloads

Published

2017-09-01

How to Cite

Nedjar, I., Mahmoudi, S., & Chikh, M. A. (2017). Content-based Medical Image Tetrieval for Liver CT Annotation. Transactions on Machine Learning and Artificial Intelligence, 5(4). https://doi.org/10.14738/tmlai.54.2985

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems