Abstract

Medical Imaging is currently a hot area of biomedical engineers, researchers and medical doctors as it is extensively used in diagnosing of human health and by health care institutes. The imaging equipment is the device, which is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and transmission process. This results in blurry image visible in low contrast. The Image De-noising System (IDs) is used as a tool for removing image noise and preserving important data. Image de-noising is one of the most interesting research areas among researchers of technology-giants and academic institutions. For Criminal Identification Systems (CIS) & Magnetic Resonance Imaging (MRI), IDs is more beneficial in the field of medical imaging. This paper proposes algorithm for de-noising medical images using different types of wavelet transform, such as Haar, Daubechies, Symlets and Biorthogonal. In this paper noise image quality has been evaluated using filter assessment parameters like Variance, Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). It has been observed form the numerical results that, the performance of proposed algorithm reduced the mean square error and achieved best value of peak signal to noise ratio (PSNR). In this paper, the wavelet based de-noising algorithm has been investigated on medical images along with threshold.

Highlights

  • Most of human-assistedcomputer applications rely on the use of digital image processing techniques, such as magnetic resonance imaging (MRI), criminal identification systems (CIS), agricultural and biological research (ABR)

  • The new algorithm has been proposed for De-noising brain medical images

  • Qualitative and quantitative analysis results reveal that the proposed algorithm reduces the mean square error (MSE) of different images with different sizes using different wavelet families for hard and soft threshold

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Summary

Introduction

Most of human-assistedcomputer applications rely on the use of digital image processing techniques, such as magnetic resonance imaging (MRI), criminal identification systems (CIS), agricultural and biological research (ABR). The use of medical imaging (MRI) in diagnosis has been greatly accepted for its non-sensitive features, low cost, the ability of constructing real-time image with improved property[1], [2].During image acquisition and transmission, it has been usually observed that random noise always occurs at another end. This noise causes problems such as a blurred vision of images, which reduce the visuality of low-contrast articles. The process of removing noise is necessary in most medical imaging equipments for the purpose of enhancing miniatures that may be concealed in the data [3][4]

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