Abstract
Nowadays, Digital image acquisition and processing techniques plays a very important role in current day medical diagnosis. During the acquisition process, there could be distortions in the images, which will negatively affect the diagnosis images. In this paper a new technique based on the hybridization of wavelet filter and center weighted median filters is proposed for denoising multiple noise (Gaussian and Impulse) images. The model is experimented on standard Digital Imaging and Communications in Medicine (DICOM) images and the performances are evaluated in terms of peak signal to noise ratio (PSNR), Mean Absolute Error (MAE), Universal Image Quality Index (UQI) and Evaluation Time (ET). Results prove that utilization of center weighted median filters in combination with wavelet thresholding filters on DICOM images deteriorates the performance. The proposed filter gives suitable results on the basis of PSNR, MSE, UQI and ET. In addition, the proposed filter gives nearly uniform and consistent results on all the test images.
Highlights
Many scientific datasets are contaminated with noise, either because of the data acquisition process, or because of naturally occurring phenomena
The peak signal to noise ratio (PSNR) and Universal Image Quality Index (UQI) value must be high for a medical image, Mean Square Error (MSE) and Evaluation Time (ET) must be less value for a better filtering algorithm
This paper describes new methods for brain image preprocessing for noise suppression based on the wavelet transform
Summary
Many scientific datasets are contaminated with noise, either because of the data acquisition process, or because of naturally occurring phenomena. Noise removal from image introduces blurring in many cases. These noises corrupt the image and often lead to incorrect diagnosis. Gaussian noise is an additive noise, which degrades image quality that originates from many microscopic diffused reflections leads to discriminate fine detail of the images in diagnostic examinations [1], [2], [3]. Denoising these noises from a noisy image has become the most important step in medical image processing
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