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Journal of Applied Sciences
  Year: 2007 | Volume: 7 | Issue: 8 | Page No.: 1224-1229
DOI: 10.3923/jas.2007.1224.1229
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Multilevel Feature Extraction and X-ray Image Classification

A. Mueen, M. Sapiyan Baba and R. Zainuddin

The need of content-based image retrieval tools increases with the enormous growth of digital medical image database. Classification of images is an important step of content-based image retrieval (CBIR). In this study, we propose a new image classification method by using multi-level image features and state-of-the-art machine learning method, Support Vector Machine (SVM). Most of the previous work in medical image classification deals with combining different global features, or local level features are used independently. We extracted three levels of features global, local and pixel and combine them together in one big feature vector. Our combined feature vector achieved a recognition rate of 89%. Large dimensional feature vector is reduced by Principal Component Analysis (PCA). Performance of two classifiers K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are also observed. Experiments are performed to verify that the proposed method improves the quality of image classification.
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  •    Medical Image Classification Using Multi-Vocabulary
  •    Improving the Annotation Accuracy of Medical Images in ImageCLEFmed2005 Using K-Nearest Neighbor (kNN) Classifier
  •    Study on an Intelligent Human Detection System for Unmanned Area Security in Ports
  •    Median and Laplacian Filters based Feature Analysis for Content Based Image Retrieval Using Color Histogram Refinement Method
How to cite this article:

A. Mueen, M. Sapiyan Baba and R. Zainuddin, 2007. Multilevel Feature Extraction and X-ray Image Classification. Journal of Applied Sciences, 7: 1224-1229.

DOI: 10.3923/jas.2007.1224.1229






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