An Approach to Adaptive Enhancement of Diagnostic X-Ray Images
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An Approach to Adaptive Enhancement of Diagnostic X-Ray Images ¨ Hakan Oktem Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland Email: [email protected]
Karen Egiazarian Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland Email: [email protected]
Jarkko Niittylahti Atostek Ltd., Hermiankatu 8D, FIN-33720 Tampere, Finland Email: [email protected]
Juha Lemmetti Atostek Ltd., Hermiankatu 8D, FIN-33720 Tampere, Finland Email: [email protected] Received 31 January 2002 and in revised form 3 October 2002 Digital radiography is a popular diagnostic imaging method. Denoising and enhancement have an important potential in obtaining as much easily interpretable diagnostic information as possible with reasonable absorbed doses of ionising radiation. Due to the increasing usage of high resolution and high precision images with a limited number of human experts, the computational efficiency of the denoising and enhancement becomes important. In this paper, a local adaptive image enhancement and simultaneous denoising algorithm for fulfilling the requirements of digital X-ray image enhancement is introduced. The algorithm is based on modification of the wavelet transform coefficients by a pointwise nonlinear transformation and reconstructing the enhanced image from the modified wavelet transform coefficients. The implementation of algorithm in software is simple, quick, and universal. Keywords and phrases: image enhancement, X-ray images, wavelet shrinkage.
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INTRODUCTION
Typically, digital X-ray images are corrupted by additive noise relatively higher with respect to conventional X-ray films. Higher SNR is possible at cost of higher absorbed doses of ionising radiation. Furthermore, image enhancement algorithms generally amplify the noise [1, 2, 3, 4]. Therefore, higher denoising performance is important in obtaining images with high visual quality using relatively lower doses of ionising radiation. The most important part of the corrupting noise is the Gaussian noise whose variance may vary with the signal level (due to sensor nonlinearity) and spatially depending on the instrumentation [2]. The visibility of some structures in medical X-ray images, especially the details that may be conveying diagnostic information, may have a vital role in providing sufficient visual information for the clinician. The visibility of relatively smaller and nonsignificant
details may be extremely important, especially in early diagnosis of cancer. Another important aspect here is the computational efficiency. The algorithm should be executed in a reasonable time since the number of human experts is limited and the workloads of radiological units are increasing especially due to the screening policies. The accuracy and resolution of X-ray images are also increasing, thus requiring more computations to be performed. Among different adaptive image enhancement methods, adaptive unsharp masking, adaptive neighbourhood filtering and enha
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