Edge enhancement

About: Edge enhancement is a research topic. Over the lifetime, 2324 publications have been published within this topic receiving 30962 citations.

Image Denoising And Enhancement Using Multiwavelet With Hard Threshold In Digital Mammographic Images

Abstract: Breast cancer continues to be a significant public health problem in the world.The diagnosing mammography method is the most effective technology for early detection of the breast cancer. However, in some cases, it is difficult for radiologists to detect the typical diagnostic signs, such as masses and microcalcifications on the mammograms . Dense region in digital mammographic images are usually noisy and have low contrast . And their visual screening is difficult to view for physicians. This paper describes a new multiwavelet methodfor noise suppression and enhancement in digital mammographic images. Initially the image is pre-processed to improve its local contrast and discriminations of subtle details.Image suppression and edge enhancement are performed based on the multiwavelet transform. At each resolution, coefficient associated with the noise is modelled and generalized by laplacian random variables. Multiwavel et can satisfy both symmetry and asymmetry which are very important characteristics in Digital image processing. The better denoising result depends on the degree of the noise, generally its energy distributed over low frequency band while both its noise and details are distributed over high frequency band and also applied hard threshold in different scale of frequency sub -bands to limit the image. This paper is proposed to indicat e the suitability of different wavelets and multiwavelet on the neighbourhood in the performance of image denoising algorithms in terms of PSNR. . Finally it compares the wavelet and multiwavelet techniques to produce the best denoised mammographic image using efficient multiwavelet algorithm with hard threshold based on the performance of image denoising algorithm in terms of PSNR values.

Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement

Abstract: In magnetic resonance imaging (MRI), the super-resolution technology has played a great role in improving image quality. The aim of this paper is to improve edges of brain MRI by incorporating the gradient information of another contrast high-resolution image. Multi-contrast images are assumed to possess the same gradient direction in a local pattern. We proposed to establish a relation model of gradient value between different contrast images to restore a high-resolution image from its input low-resolution version. The similarity of image patches is employed to estimate intensity parameters, leading a more accurate reconstructed image. Then, an iterative back-projection filter is applied to the reconstructed image to further increase the image quality. The new approach is verified on synthetic and real brain MRI images and achieves higher visual quality and higher objective quality criteria than the compared state-of-the-art super-resolution approaches. The gradient information of the multi-contrast MRI images is very useful. With a proper relation model, the proposed method enhances image edges in MRI image super-resolution. Improving the MRI image resolution from very low-resolution observations is challenging. We tackle this problem by first modeling the relation of gradient value in multi-contrast MRI and then performing fast supper-resolution methods. This relation model may be helpful for other MRI reconstruction problems.

Edge Detection Techniques in Processing Digital Images: Investigation of Canny Algorithm and Gabor Method

Abstract: Edge detection is a primary function in image processing. It is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. In this paper we are going to have a short introduction to edge detection basic concepts and continue with 2 popular methods: Canny edge detection and Gabor method.

The AAPM/RSNA physics tutorial for residents: digital fluoroscopy.

Abstract: A digital fluoroscopy system is most commonly configured as a conventional fluoroscopy system (tube, table, image intensifier, video system) in which the analog video signal is converted to and stored as digital data. Other methods of acquiring the digital data (eg, digital or charge-coupled device video and flat-panel detectors) will become more prevalent in the future. Fundamental concepts related to digital imaging in general include binary numbers, pixels, and gray levels. Digital image data allow the convenient use of several image processing techniques including last image hold, gray-scale processing, temporal frame averaging, and edge enhancement. Real-time subtraction of digital fluoroscopic images after injection of contrast material has led to widespread use of digital subtraction angiography (DSA). Additional image processing techniques used with DSA include road mapping, image fade, mask pixel shift, frame summation, and vessel size measurement. Peripheral angiography performed with an automatic moving table allows imaging of the peripheral vasculature with a single contrast material injection.

An Adaptive Histogram Equalization Based Local Technique for Contrast Preserving Image Enhancement

Abstract: The main purpose of image enhancement is to improve certain characteristics of an image to improve its visual quality. This paper proposes a method for image contrast enhancement that can be applied to both medical and natural images. The proposed algorithm is designed to achieve contrast enhancement while also preserving the local image details. To achieve this, the proposed method combines local image contrast preserving dynamic range compression and contrast limited adaptive histogram equalization (CLAHE). Global gain parameters for contrast enhancement are inadequate for preserving local image details. Therefore, in the proposed method, in order to preserve local image details, local contrast enhancement at any pixel position is performed based on the corresponding local gain parameter, which is calculated according to the current pixel neighborhood edge density. Different image quality measures are used for evaluating the performance of the proposed method. Experimental results show that the proposed method provides more information about the image details, which can help facilitate further image analysis.