Digital Image Processing

H. S. Bhadauria ¹, Annapurna Singh ²

Affiliations
1 Department of Electrical Engineering, Indian Institute of Technology, Roorkee-247667, IN
2 Department of Computer Sc. & Engineering, G. B. Pant Engineering College, Pauri-Garhwal, OM

Abstract

This paper presents a comparative assessment of various denoising methods on brain CT images. The paper quantitatively compares total of four denoising methods namely wiener filter, median filter, anisotropic diffusion and total variation (TV). The focus of this work is to compare these methods not only for the suppression of noise but also for the preservation of fine details and edges on brain CT images. The experimental results show that the wiener filter shows the best performance in terms of perceptual quality, noise suppression and edge preservation. It yields the higher values of SNR, PSNR, UQI and EKI as compared to other denoising methods. This is an evidence of the maximum noise suppression with significant edges and fine details preservation.Total variation method induces some staircase effect and loss of fine details. Wavelet based method yields better denoising particularly in homogenous regions but does not gives better results in edgy regions and anisotropic diffusion method shows blurring effect and thus edges and fine details are lost.

Keywords

Computed Tomography (CT), Total Variation (TV), Anisotropic Diffusion (AD).

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H. S. Bhadauria , M. L. Dewal

Abstract

The development of computer-aided diagnosis (CAD) has been extended to various medical imaging modalities including computer Tomography (CT), magnetic resonance imaging, nuclear medicine etc. Medical image edge detection is an important work for diagnosis of various lung diseases in thoracic CT images. When detecting the edge based on wavelet transform, the edge detection result is poor because of the noise influence. This paper proposed a new edge detection algorithm based on wavelet transform and canny operator. In the wavelet domain, the low-frequency edges are detected by canny operator, while the high-frequency edges are detected by solving the maximum points of local wavelet coefficient model to restore edges after reducing the noise by wavelet. Then, both sub-images edges are fused according to fusion rules. A noisy CT image of abnormal lung infected by Honeycombing is used to evaluate the performance of algorithms.

Keywords

Lung CT Images, Edge Detection, Wavelet Transform, Canny Operator.

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