Proceedings ArticleDOI
Medical image restoration with different types of noise
M.G. Sánchez,Vicente Vidal,Gumersindo Verdú,P. Mayo,F. Rodenas +4 more
- Vol. 2012, pp 4382-4385
Reads0
Chats0
TLDR
This work proposes a method designed to reduce the Gaussian, the impulsive and speckle noise and combined noise, called PGNDF, which combines a non-linear diffusive filter with a peer group with fuzzy metric technique.Abstract:
The images obtained by X-Ray or computed tomography (CT) in adverse conditions may be contaminated with noise that can affect the detection of diseases. A large number of image processing techniques (filters) have been proposed to remove noise. These techniques depend on the type of noise present in the image. In this work, we propose a method designed to reduce the Gaussian, the impulsive and speckle noise and combined noise. This filter, called PGNDF, combines a non-linear diffusive filter with a peer group with fuzzy metric technique. The proposed filter is able to reduce efficiently the image noise without any information about what kind of noise might be present. To evaluate the filter performance, we use mammographic images from the mini- MIAS database which we have damaged by adding Gaussian, impulsive and speckle noises of different magnitudes. As a result, the proposed method obtains a good performance in most of the different types of noise.read more
Citations
More filters
Journal ArticleDOI
Noise Issues Prevailing in Various Types of Medical Images
TL;DR: The basic definition, history, usage and type of noise affecting some of the major types of imaging modalities affecting medical images, remote sensing images and natural images are included.
Proceedings ArticleDOI
Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection
TL;DR: Wang et al. as discussed by the authors proposed a play-and-plug medical image denoising framework, namely Lesion-Inspired Denoising Network (LIDnet), to collaboratively improve both denoizing performance and detection accuracy of denoised medical images.
Book ChapterDOI
Performance Measurement of Various Hybridized Kernels for Noise Normalization and Enhancement in High-Resolution MR Images
TL;DR: It is observed that each of those hybridized kernels outperformed the type of noise on which they are experimented, and the mean computational time of each kernel is also been presented in the results.
Proceedings ArticleDOI
Automatic Detection and Classification of Solitary Pulmonary Nodules from Lung CT Images
TL;DR: A novel method is proposed that detects and categorizes solitary pulmonary nodules responsible for lung cancer from lung CT images and reduces variability in detection by automatic segmentation and classification of nodules.
References
More filters
Journal ArticleDOI
Nonlinear total variation based noise removal algorithms
TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.
Journal ArticleDOI
De-noising by soft-thresholding
TL;DR: The authors prove two results about this type of estimator that are unprecedented in several ways: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures.
Book
The image processing handbook
TL;DR: In this paper, the acquisition and use of digital images in a wide variety of scientific fields is discussed. But the focus is on high dynamic range imaging in more than two dimensions.
Journal ArticleDOI
Image selective smoothing and edge detection by nonlinear diffusion. II
TL;DR: In this article, a new version of the Perona and Malik theory for edge detection and image restoration is proposed, which keeps all the improvements of the original model and avoids its drawbacks.
Book
Introduction to Inverse Problems in Imaging
Mario Bertero,Patrizia Boccacci +1 more
TL;DR: Part 2 Linear inverse problems: examples of linear inverse problems singular value decomposition (SVD) inversion methods revisited Fourier based methods for specific problems comments and concluding remarks.