Median filter

About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.

Preprocessing of two-dimensional gel electrophoresis images.

Abstract: Proteomics produces a huge amount of two-dimensional gel electrophoresis images. Their analysis can yield a lot of information concerning proteins responsible for different diseases or new unidentified proteins. However, an automatic analysis of such images requires an efficient tool for reducing noise in images. This allows proper detection of the spots' borders, which is important in protein quantification (as the spots' areas are used to determine the amounts of protein present in an analyzed mixture). Also in the feature-based matching methods the detected features (spots) can be described by additional attributes, such as area or shape. In our study, a comparison of different methods of noise reduction is performed in order to find out a method best suited for reducing noise in gel images. Among the compared methods there are the classical methods of linear filtering, e.g., the mean and Gaussian filtering, the nonlinear method, i.e., median filtering, and also the methods better suited for processing of nonstationary signals, such as spatially adaptive linear filtering and filtering in the wavelet domain. The best results are obtained by filtering of gel images in the wavelet domain, using the BayesThresh method of threshold value determination.

Edge detection in medical ultrasound images using adjusted Canny edge detection algorithm

Abstract: Ultrasound medical images are very important component of the diagnostics process. They are widely used since ultrasound is a non-invasive and non-ionizing diagnostics method. As a part of image analysis, edge detection is often used for further segmentation or more precise measurements of elements in the picture. Edges represent high frequency components of an image. Unfortunately, ultrasound images are subject to degradations, especially speckle noise which is also a high frequency component. That poses a problem for edge detection algorithms since filters for noise removal also degrade edges. Canny operator is widely used as an excellent edge detector, however it also includes Gaussian smoothing element that may significantly soften edges. In this paper we propose a modified Canny algorithm where Gaussian smoothing is replaced by modified median filter that successfully removes speckle noise with little degradation of edges followed by weak weighted smoothing filter that in a controlled way removes other noise, again with insignificant damage to the edges. Our proposed algorithm was tested on standard benchmark image and compared to other approaches from literature where it proved to be successful in precisely determining edges of internal organs.

Detection of brain tumor from MRI images by using segmentation & SVM

Abstract: In this paper we propose adaptive brain tumor detection, Image processing is used in the medical tools for detection of tumor, only MRI images are not able to identify the tumorous region in this paper we are using K-Means segmentation with preprocessing of image. Which contains de-noising by Median filter and skull masking is used. Also we are using object labeling for more detailed information of tumor region. To make this system an adaptive we are using SVM (Support Vector Machine), SVM is used in unsupervised manner which will use to create and maintain the pattern for future use. Also for patterns we have to find out the feature to train SVM. For that here we have find out the texture feature and color features. It is expected that the experimental results of the proposed system will give better result in comparison to other existing systems.

Noise dependent filter

Abstract: A communication device includes a noise flattening filter having a filter response that dynamically adjusts based on the current noise spectrum in a wireless channel. The noise spectrum of the wireless channel is estimated and used to determine a noise classification for the channel. A noise flattening filter response is then selected based upon the noise classification for use in filtering signals received from the channel. The filtered signals are then delivered to an equalizer for further processing.