Andere Kunden interessierten sich auch für
![Image De-Noising Based On Patch Alignment In Complex Wavelet Domain]( "Image De-Noising Based On Patch Alignment In Complex Wavelet Domain")
Image De-Noising Based On Patch Alignment In Complex Wavelet Domain26,99 €![Approches de débruitage pour les images biomédicales]( "Approches de débruitage pour les images biomédicales")
Approches de débruitage pour les images biomédicales43,99 €![VLSI Architecture for Removing Impulse Noise in Images]( "VLSI Architecture for Removing Impulse Noise in Images")
VLSI Architecture for Removing Impulse Noise in Images26,99 €![Brain MR Image Segmentation]( "Brain MR Image Segmentation")
Brain MR Image Segmentation55,99 €![Detection of Medical Images (MRI)& Many Electronic Applications]( "Detection of Medical Images (MRI)& Many Electronic Applications")
Detection of Medical Images (MRI)& Many Electronic Applications26,99 €![Segmentation of Brain Tumor From MRI Images]( "Segmentation of Brain Tumor From MRI Images")
Segmentation of Brain Tumor From MRI Images26,99 €![Non linear Detection of Lesion with ANOVA Technique For Multi MRI images]( "Non linear Detection of Lesion with ANOVA Technique For Multi MRI images")
Non linear Detection of Lesion with ANOVA Technique For Multi MRI images26,99 €
Over the past two decades, medical imaging and diagnostic techniques have gained immense attraction due to the rapid development in computing, internet, data storage and wireless technology. The reflection of these advancements has become evident in the field of medicine and medical sciences which enables the diagnosis and treatment of various diseases in a more fruitful manner. Furthermore, medical imaging is frequently justified in the follow up of a disease which is already diagnosed and treated. Medical images like any other form of imaging techniques are susceptible to noise and artifacts. The major challenge in the process of medical imaging is to obtain an image without loss of any significant information. It is highly probable that the images obtained may be corrupted by noise or artifacts during the process of acquisition and/or further processing stages. Noise can be described as the random change of the original pixel value. Noise reduces the image quality and is especially significant when the objects being imaged are small and have relatively low contrast. Hence, de-noising of medical images is indispensable and has become a mandatory pre-processing stage.