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A Selection and Reduction Approach for the Optimization of Ultrasound Carotid Artery Images Segmentation

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Machine Learning in Healthcare Informatics

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 56))

Abstract

The segmentation of the carotid artery wall is an important aid to sonographers when measuring intima-media thickness (IMT). Automated and completely user-independent segmentation techniques are gaining increasing importance, because they avoid the bias coming from human interactions. However, automated techniques still underperform semi-automated IMT measurement methods. Automated techniques cannot reproduce human expertise in selecting the optimal point where IMT should be measured. Hence, superior intelligence must be embedded into automated techniques in order to overcome the performance limitations. A possible solution is to extract more information from the image, which could be obtained by an accurate analysis of the image at pixel level. In this study, we applied a feature selection and reduction approach to ultrasound carotid images, and measured 141 features for each image pixel and supposed that a pixel could belong to one of three classes: artery lumen, intima or media layer, or the adventitia layer. Among several approaches that are available for dimensional reduction, we chose to test three based on the Rough-Set Theory (RST): the QuickReduct Algorithm (QRA), the Entropy-Based Algorithm (EBR) and the Improved QuickReduct Algorithm (IQRA). QRA achieved the best performance and correctly classified 97.5 % of the pixels on a reduced testing image dataset and about 91.5 % for a large validation dataset. On average, QRA reduced the complexity of the system from 141 to 8 or 9 features. This result could represent a pilot study for developing an intelligent pre-classifier to improve the image segmentation performance of automated techniques in carotid ultrasound imaging.

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Author information

Authors and Affiliations

  1. Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy

    Samanta Rosati, Gabriella Balestra & Filippo Molinari

  2. Department of ECE, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  3. Faculty of Engineering, Department of Biomedical Engineering, University of Malaya, Kuala Lumpur, Malaysia

    U. Rajendra Acharya

  4. Global Biomedical Technologies, Inc, Roseville, CA, USA

    Jasjit S. Suri

  5. Biomedical Engineering Department, Idaho State University, Pocatello, ID, USA

    Jasjit S. Suri

Authors
  1. Samanta Rosati
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  2. Gabriella Balestra
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  3. Filippo Molinari
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  4. U. Rajendra Acharya
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  5. Jasjit S. Suri
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Corresponding author

Correspondence to Filippo Molinari .

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Editors and Affiliations

  1. Department of Computer Science, Louisiana Tech University, Ruston, Louisiana, USA

    Sumeet Dua

  2. Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  3. Department of Health Informatics and Information Management, Louisiana Tech University, Ruston, Louisiana, USA

    Prerna Dua

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© 2014 Springer-Verlag Berlin Heidelberg

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Rosati, S., Balestra, G., Molinari, F., Rajendra Acharya, U., Suri, J.S. (2014). A Selection and Reduction Approach for the Optimization of Ultrasound Carotid Artery Images Segmentation. In: Dua, S., Acharya, U., Dua, P. (eds) Machine Learning in Healthcare Informatics. Intelligent Systems Reference Library, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40017-9_14

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  • DOI: https://doi.org/10.1007/978-3-642-40017-9_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40016-2

  • Online ISBN: 978-3-642-40017-9

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