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Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
median filter a filter which takes the median of the various input signal components, or in the case of an image, the median of all the pixel intensity values within the neighborhood of the current pixel, the median being defined as the center value of the ordered signal components. medical imaging a multi-disciplinary field that uses imaging scanners to reveal the internal anatomic structure and physiologic processes of the body to facilitate clinical diagnoses. See X-ray CT, magnetic resonance imaging, ultrasound, positron emission tomography, and radiography. medium-scale integration (MSI) (1) an early level of integration circuit fabrication that allowed approximately between 12 and 100 gates on one chip. (2) a single packaged IC device with 12 to 99 gate-equivalent circuits. megacell a cell with the radius of 20-100 km. See also cell. megaflop (MFLOP) one million floating point operations per second. Usually applied as a
Image Enhancement in Spatial Domain
Published in Vipin Tyagi, Understanding Digital Image Processing, 2018
In smoothing non-linear filters, the intensity value of a pixel is replaced by the value of the pixel of the filter area selected by some ranking. A very common non-linear smoothing filter is the median filter, in which the value of a pixel is replaced by the median intensity value of all pixels’ intensity in the neighborhood of the pixel. To implement a median filter, initially all the values on the defined neighborhood, including the pixel being processed, are sorted. The median value is selected, and the pixel being processed is assigned this median intensity value. Median filters are very useful in removing salt-and-pepper noise, in which the intensity of certain pixels is very different from the neighboring pixels. By applying median filters, these noisy isolated pixels are replaced by the value more like the neighbors, as extreme values are not considered in this filter. In a 3×3 neighborhood, the 5th largest value is the median and that value will be used for replacement at the pixel being processed. Similarly, in a 5×4 neighborhood, 13th highest value will be considered. In the case of median filters, isolated pixel clusters which are very bright or very dark in comparison to their neighbors and cover an area of less than m2/2 are removed by an m×m size median filter.
The Role(s) of Computers
Published in F. Brent Neal, John C. Russ, Measuring Shape, 2017
Blurring an image with a Gaussian smoothing filter is sometimes applied in an attempt to reduce speckle noise but is a poor choice because it blurs steps and edges, and can shift or distort boundaries. A median filter, in which the intensity values of pixels in a small neighborhood are ranked into order and the median value in the ordered list replaces the original value of the pixel at the center of the neighborhood, is a preferred tool for reducing random noise. The process is repeated for every pixel in the image, always using the original pixel values. Extreme values are replaced without shifting or blurring steps and edges. Most programs implement the median using an adjustable-size square neighborhood, which is convenient for programming, although an approximately round neighborhood is preferred to avoid directional artifacts.
A novel image recognition using Fuzzy C-Means and content-based fabric image retrieval
Published in The Imaging Science Journal, 2023
A. Meenakshi, A. P. Janani, S. Devi Mahalakshmi, S. Vanitha Sivagami
Initially, the fabric images are pre-processed by the median filter, which removes the high-frequency elements from the images[25]. The main reason for utilizing a median filter is that it preserves the edges. This operation is carried out for every pixel, in which the mean pixel score is computed and the value of the centre pixel is modified accordingly. This way of pre-processing smoothens the fabric image. The two important features of fabric images are colour and texture. The fabric images have to be pre-processed in such a way that the colour feature can be extracted successfully. This work converts the fabric image to HSV (Hue Saturation Value) colour model from the RGB (Red Green Blue) because the RGB information is concentrated. The HSV model, on the other hand, consistently distributes colour, allowing for the extraction of colour information. The following subsection describes the feature extraction process. Figure 1 displays the proposed model's architecture.
Automatic Segmentation of Lung Tumor from X-Ray Images Using Advance Novel Semantic Approach
Published in IETE Journal of Research, 2021
K. Vijila Rani, S. Albert Jerome, P. Josephin Shermila, L. K. Shoba, M. Eugine Prince
Median filter is a nonlinear method to eliminate image noise and preserve image distinctions effectively. In specific, it is an effective way to reduce salt and pepper noise. The pixels of the image are shifted and the average values of the neighboring pixels are replaced by each value. The median is calculated in the number order from the windows and all values of the pixels are sorted and the middle pixel is replaced. The not-linear technique provides an ideal alternative to linear filtering, eliminating noise and shielding the edge. In many median filters are used as backbones for their ability to denounce the reduction of noise and measurement efficiency. It eliminates noise and renders the picture transparent by filtering. A histogram displays the frequency of the color level in the given picture as a graphic. Colors of higher or lower frequency are observed in the case of low contrast images or with no occurrence. In order to increase the pixel intensity, histogram equalization is applied. Histogram equalization is the most widely used way to enhance contrast.
Handwritten optical character recognition by hybrid neural network training algorithm
Published in The Imaging Science Journal, 2019
This section shows the pre-processing steps of the input image, in which the features of the image are extracted by removing the foreground regions for further processing. The initial step in the pre-processing is removing the noise from the input image. Here, the median filter is used for removing the noise from the input image. The advantage of using a median filter is that it preserves edges while removing noise. After that, the image is handled in two ways, such as ROI extraction and resizing. The ROI extraction is used to identify the selected subset of image samples from the input image. After performing the ROI extraction, the input image is applied to resizing, in which the different dimensions of the image pixels are reduced to fixed size pixels. Then, the resized form of the input image can be represented as follows:where, -th column of the pixel can be varied from 1 to and is a row of pixels can be varied from 1 to . Then, denotes the resized image and pixel of the resized image with and pixel row and pixel column are represented as.