Enhancement of Chest Radiograph Based on Wavelet Transform

  • Authors:

  • Zhenghao Shi;Lifeng He;Tsuyoshi Nakamura;Hidenori Itoh

  • Affiliations:

  • School of Computer Science and Engineering, Nagoya Institute of Technology, Japan and School of Computer Science and Engineering, Xi'an University of Technology, China;School of Information Science and Technology, Aichi Prefectural University, Japan;School of Computer Science and Engineering, Nagoya Institute of Technology, Japan;School of Computer Science and Engineering, Nagoya Institute of Technology, Japan

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
  • Year:
  • 2009

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Abstract

The effect of anatomical noise is one of the major challenges for the early detection of pulmonary nodules in chest radiograph. A method aimed at eliminating these anatomical noises while enhancing contrast of anatomical feature is presented. The method is based on local modification of gradient magnitude values provided by the redundant dyadic wavelet transform. It includes two key steps. The first one is to threshold wavelet coefficients, which is accomplished by using a threshold strategy. The purpose of this operation is to reduce the effect of background and anatomical noise on the region of interesting in the chest radiograph. The second one is to do a normalization operation for all retained wavelet coefficients at a same scale. The purpose of this operation is to ensure that the enhanced image is not sensitive to the variance of radiograph acquirement environment. Experimental results (performed under different conditions.) indicate the efficiency and the effectiveness of the proposed method in radiography enhancement.