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).

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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 Engineering and Computing Sciences, 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