title: Investigating the Effectiveness of Wavelet Approximations in Resizing Images for Ultrasound Image Classification.
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.
authors: King Abdulaziz University, Jeddah, Saudi Arabia. umarmanzoor@gmail.com.
authors: Umar
authors: http://orcid.org/0000-0001-7602-1914
authors: U
authors: Manzoor
authors: School of Computing, Science and Engineering, The University of Salford, Greater Manchester, Salford, UK.
authors: Samia
authors: S
authors: Nefti
authors: School of Computing, Science and Engineering, The University of Salford, Greater Manchester, Salford, UK.
authors: Milella
authors: M
authors: Ferdinando
mesh_terms: D003952:Diagnostic Imaging / Q000145:classification*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D014463:Ultrasonography; D058067:Wavelet Analysis
keywords: Feature extraction; Image processing; Image resizing; Neural networks; Ultrasound classification; Wavelet transformation