In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images. In the proposed method median filter is modified by adding more features. Experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE). Experimental results of magnetic resonance (MR) image and ultrasound image demonstrate that the proposed algorithm is comparable to popular image smoothing algorithms.Key words: Magnetic resonance image; Ultrasound image; PSNR; SNR; RMSE.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5544 J. Sci. Res. 3 (1), 81-89 (2011)
The main objective of medical image segmentation is to extract and characterize anatomical structures with respect to some input features or expert knowledge. This paper describes a way of medical image segmentation based on level set method to extract the region of interest of various medical images such as magnetic resonance imaging (MRI), computer tomography (CT), and positron emission tomography (PET) image. The overall technique of the paper is divided into three steps. The first step is to do threshold of the input image to make the entire pixel under threshold value to 0 and others to take the value as original image. This helps to keep original properties of original image and keep all value fixed as original image except the pixel that are not necessary for our analysis. Next, a morphological technique is used to remove some small ignorable parts. Finally we apply variational level set method for final segmentation. The experimental results show the efficacy of the proposed method.
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