Median filter

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

Identification of Outliers in Hyperspectral Raman Image Data by Nearest Neighbor Comparison

Abstract: A new algorithm for removal of cosmic spikes from hyperspectral Raman image data sets is presented. Spectra in a 3 × 3 pixel neighborhood are used to identify outlier-contaminated data points in the central pixel of that neighborhood. A preliminary despiking of the neighboring spectra is performed by median filtering. Correlations between the central pixel spectrum and its despiked neighbors are calculated, and the most highly correlated spectrum is used to identify outliers. Spike-contaminated data are replaced using results of polynomial interpolation. Because the neighborhood contains spectra obtained in three different frames, even large multi-pixel spikes are identified. Spatial, spectral, and temporal variation in signal is used to accurately identify outliers without the acquisition of any spectra other than those needed to generate the image itself. Sharp boundaries between regions of high chemical contrast do not interfere with outlier identification.

Analysis and identification of kidney stone using Kth nearest neighbour (KNN) and support vector machine (SVM) classification techniques

Abstract: Kidney stone detection is one of the sensitive topic now-a-days. There are various problem associates with this topic like low resolution of image, similarity of kidney stone and prediction of stone in the new image of kidney. Ultrasound images have low contrast and are difficult to detect and extract the region of interest. Therefore, the image has to go through the preprocessing which normally contains image enhancement. The aim behind this operation is to find the out the best quality, so that the identification becomes easier. Medical imaging is one of the fundamental imaging, because they are used in more sensitive field which is a medical field and it must be accurate. In this paper, we first proceed for the enhancement of the image with the help of median filter, Gaussian filter and un-sharp masking. After that we use morphological operations like erosion and dilation and then entropy based segmentation is used to find the region of interest and finally we use KNN and SVM classification techniques for the analysis of kidney stone images.

Real-time 2D–3D filtering using order statistics based algorithms

Abstract: The paper presents a review of the author’s own results obtained in the last several years. Some examples of real-time processing of 2D and 3D images are described. In particular, we discuss the noise model and objective criteria that can be applied to characterize the performance of the processing algorithms. Several proposed algorithms based on RM approach are compared with other known ones, demonstrating the advantages in noise suppressing and preservation of fine image details and edges. A number of 2D and 3D image denoising filters are implemented on DSP, realizing real-time mode in the image processing. The performances of the proposed processing algorithms and the known ones are discussed and evaluated here.