Uncertainty as key element in the analysis of X–ray angiography images

The X–ray angiography images are routinely used to assess the blood vessels. The acquisition procedure considers a medical imaging system which allows obtaining views of the vessel while the blood flows thought them. The X–ray source is influenced on the region to be viewed and then, the projection of the all anatomical structures in the champ of view is shown through an image intensifier. The information of the blood vessel is impacted for the other structures. Additionally, the blood and the contrast product required in the acquisition are not mixed homogeneously, producing artifacts in the images. Finally, the noise is also an impact factor in the quality of the angiography images. In the coronary vessel case, the branches of the network are superposed. In this paper, an enhancement procedure to diminish the uncertainty associated to X–ray angiography images is reported. The relation between two versions of the angiograms is determined using a fuzzy connector considering that this relation diminishes the images intrinsic uncertainty. These versions correspond with images filtered with low-pass and high-pass image filters, respectively. The technique is tested with images of the coronary and kidney vessels. The qualitative results show a good enhanced of the angiography images

Implementation of Impulse Noise Reduction Method to Color Images using Fuzzy Logic

Image Processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications. Impulse noise reduction method is one of the critical techniques to reduce the noise in color images. In this paper the impulse noise reduction method for color images by using Fuzzy Logic is implemented. Generally Grayscale algorithm is used to filter the impulse noise in corrupted color images by separate the each color component or using a vector-based approach where each pixel is considered as a single vector. In this paper the concepts of Fuzzy logic has been used in order to distinguish between noise and image characters and filter only the corrupted pixels while preserving the color and the edge sharpness. Due to this a good noise reduction performance is achieved. The main difference between this method and other classical noise reduction methods is that the color information is taken into account to develop a better impulse noise detection a noise reduction that filters only the corrupted pixels while preserving the color and the edge sharpness. The Fuzzy based impulse noise reduction method is implemented on set of selected images and the obtained results are presented

Improved image speckle noise reduction and novel dispersion cancellation in Optical Coherence Tomography

Optical coherence tomography (OCT) is an innovative modern biomedical imaging technology that allows in-vivo, non-invasive imaging of biological tissues. At present, some of the major challenges in OCT include the need for fast data acquisition system for probing fast developing biochemical processes in biological tissue, for image processing algorithms to reduce speckle noise and to remove motion artefacts, and for dispersion compensation to improve axial resolution and image contrast. To address the need for fast data acquisition, a novel, high speed (47,000 A-scans/s), ultrahigh axial resolution (3.3μm) Fourier Domain Optical Coherence Tomography (FD-OCT) system in the 1060nm wavelength region has been built at the University of Waterloo. The system provides 3.3μm image resolution in biological tissue and maximum sensitivity of 110 dB. Retinal tomograms acquired in-vivo from a human volunteer and a rat animal model show clear visualization of all intra-retinal layers and increased penetration into the choroid. OCT is based on low-coherence light interferometry. Thus, image quality is dependent on the spatial and temporal coherence properties of the optical waves back-scattered from the imaged object. Due to the coherent nature of light, OCT images are contaminated with speckle noise. Two novel speckle noise reduction algorithms based on interval type II fuzzy sets has been developed to improve the quality of the OCT images. One algorithm is a combination of anisotropic diffusion and interval type II fuzzy system while the other algorithm is based on soft thresholding wavelet coefficients using interval type II fuzzy system. Application of these novel algorithms to Cameraman test image corrupted with speckle noise (variance=0.1) resulted in a root mean square error (RMSE) of 0.07 for both fuzzy anisotropic diffusion and fuzzy wavelet algorithms. This value is less compared to the results obtained for Wiener (RMSE=0.09), adaptive Lee (RMSE=0.09), and median (RMSE=0.12) filters. Applying the algorithms to optical coherence tomograms acquired in-vivo from a human finger-tip show reduction in the speckle noise and image SNR improvement of ~13dB for fuzzy anisotropic diffusion and ~11db for fuzzy wavelet. Comparison with the Wiener (SNR improvement of ~3dB), adaptive Lee (SNR improvement of ~5dB) and median (SNR improvement of ~5dB) filters, applied to the same images, demonstrates the better performance of the fuzzy type II algorithms in terms of image metrics improvement. Micrometer scale OCT image resolution is obtained via use of broad bandwidth light sources. However, the large spectral bandwidth of the imaging beam results in broadening of the OCT interferogram because of the dispersive properties of the imaged objects. This broadening causes deterioration of the axial resolution and as well as loss of contrast in OCT images. A novel even-order dispersion cancellation interferometry via a linear, classical interferometer has been developed which can be further expanded to dispersion canceled OCT