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Investigating the Effectiveness of Wavelet Approximations in Resizing Images for Ultrasound Image Classification

  • Systems-Level Quality Improvement
  • Published:
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Abstract

Images are difficult to classify and annotate but the availability of digital image databases creates a constant demand for tools that automatically analyze image content and describe it with either a category or a set of variables. Ultrasound Imaging is very popular and is widely used to see the internal organ(s) condition of the patient. The main target of this research is to develop a robust image processing techniques for a better and more accurate medical image retrieval and categorization. This paper looks at an alternative to feature extraction for image classification such as image resizing technique. A new mean for image resizing using wavelet transform is proposed. Results, using real medical images, have shown the effectiveness of the proposed technique for classification task comparing to bi-cubic interpolation and feature extraction.

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Authors and Affiliations

  1. King Abdulaziz University, Jeddah, Saudi Arabia

    Umar Manzoor

  2. School of Computing, Science and Engineering, The University of Salford, Greater Manchester, Salford, UK

    Samia Nefti & Milella Ferdinando

Authors
  1. Umar Manzoor
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  2. Samia Nefti
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  3. Milella Ferdinando
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Correspondence to Umar Manzoor.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Manzoor, U., Nefti, S. & Ferdinando, M. Investigating the Effectiveness of Wavelet Approximations in Resizing Images for Ultrasound Image Classification. J Med Syst 40, 221 (2016). https://doi.org/10.1007/s10916-016-0573-7

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  • DOI: https://doi.org/10.1007/s10916-016-0573-7

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