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Content-Based Retrieval of Focal Liver Lesions Using Geometrical and Textural Features of Multi-Phase CT-Scan Images

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Innovation in Medicine and Healthcare

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 242))

Abstract

The most critical step in content-based medical image retrieval systems is feature extraction. The objective of the feature-extraction phase is to discriminate between different types of lesions. Preparing useful features improves diagnosis by an image retrieval system and successful treatment. We propose a new set of geometrical features based on the description of different types of tumors and integrate them with textural elements to enhance the discrimination of five kinds of focal liver lesions. The abnormal region is divided into three sections, including central, middle, and border regions. The textural features are obtained in each part individually, while the geometrical characteristics are calculated for the whole zone of a lesion. Evaluation of the results shows an improvement of prec@6 and mAP, which is attributed to the proposed geometric characteristics. Moreover, our method increases prec@6 by 3.9% compared to recent researches.

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Acknowledgements

Authors would like to thank Prof. Hongjie Hu in Sir Run Run Shaw Hospital, Medical School, Zhejiang University, for providing us CT data and advice on data processing. This research was supported in part by the Grant-in-Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 18H03267.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Shahed University, Tehran, Iran

    Saeed Moslehi & Amir Hossein Foruzan

  2. Intelligent Image Processing Lab, College of Information, Science and Engineering, Ritsumeikan University, Shiga, Japan

    Yen-Wei Chen

  3. Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang, China

    Hongjie Hu

Authors
  1. Saeed Moslehi
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  2. Amir Hossein Foruzan
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  3. Yen-Wei Chen
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  4. Hongjie Hu
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Corresponding author

Correspondence to Amir Hossein Foruzan .

Editor information

Editors and Affiliations

  1. Ritsumeikan University, Kyoto, Japan

    Yen-Wei Chen

  2. Ritsumeikan University, Kyoto, Japan

    Satoshi Tanaka

  3. Bournemouth University, Poole, UK

    Robert J. Howlett

  4. University of Technology Sydney, Sydney, Australia

    Lakhmi C. Jain

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Moslehi, S., Foruzan, A.H., Chen, YW., Hu, H. (2021). Content-Based Retrieval of Focal Liver Lesions Using Geometrical and Textural Features of Multi-Phase CT-Scan Images. In: Chen, YW., Tanaka, S., Howlett, R.J., Jain, L.C. (eds) Innovation in Medicine and Healthcare. Smart Innovation, Systems and Technologies, vol 242. Springer, Singapore. https://doi.org/10.1007/978-981-16-3013-2_21

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