Gray Level Histogram Width Tissue Characterization

Gray Level Histogram Width Tissue Characterization

Robust Gray-Level Histogram Gaussian Characterisation

One of the most utilised criteria for segmenting an image is the gray level values of the pixels in it. The information for identifying similar gray values is usually extracted from the image histogram. We have analysed the problems that may arise when the histogram is automatically characterised in terms of multiple Gaussian distributions and solutions have been proposed for special situations...

Multi-modal gray-level histogram modeling and decomposition

In this paper, we present a novel multi-modal histogram thresholding method in which no a priori knowledge about the number of clusters to be extracted is needed. The proposed method combines regularization and statistical approaches. By converting the approaching histogram thresholding problem to the mixture Gaussian density modeling problem, threshold values can be estimated precisely accordi...

Histogram analysis- a useful tool for tissue characterization in brain CT

Introduction: Pixel value in computed tomography (CT) gives the average linear attenuation coefficient of the scanned material in the path of the x-ray beam, being normalized to that of water. It is known that attenuation coefficient or HU value is a function of the chemical characteristic of the material and of the x-ray energy. The CT image shows the HU value by a shade of gr...

Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram

Thresholding is a popular method of image segmentation. Many thresholding methods utilize only the gray level information of pixels in the image, which may lead to poor segmentation performance because the spatial correlation information between pixels is ignored. To improve the performance of thresolding methods, a novel two-dimensional histogram—called gray level-local variance (GLLV) histogr...