scholarly journals General Adaptive Neighborhood Image Processing for Biomedical Applications

10.5772/18614 ◽  
2011 ◽  
Author(s):  
Johan Debayle ◽  
Jean-Charles Pinoli
Keyword(s):  
Image Processing ◽  
Biomedical Applications ◽  
Adaptive Neighborhood

scholarly journals Rotation Covariant Image Processing for Biomedical Applications

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Henrik Skibbe ◽  
Marco Reisert
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Biomedical Applications ◽  
Feature Detection ◽  
Mathematical Framework ◽  
3D Image ◽  
Image Description ◽  
Volumetric Images ◽  
Object Feature ◽  
High Level

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

Enhancement of Mammographic Features by Optimal Adaptive Neighborhood Image Processing

1986 ◽  
Vol 5 (1) ◽  
pp. 8-15 ◽  
Author(s):  
Atam P. Dhawan ◽  
Gianluca Buelloni ◽  
Richard Gordon
Keyword(s):  
Image Processing ◽  
Adaptive Neighborhood

Algorithm for Improving Image Denoising Based on Adaptive Wavelet Transform

2014 ◽  
Vol 912-914 ◽  
pp. 1134-1137
Author(s):  
Xiang Shi Wang
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Specific Property ◽  
Threshold Function ◽  
Natural Image ◽  
Wavelet Domain ◽  
Adaptive Wavelet ◽  
Threshold Functions ◽  
Adaptive Wavelet Transform ◽  
Adaptive Neighborhood

The denoising of a natural image is the important area in image processing. As a tool of image processing, wavelet transform is widly applied in removing of gauss noise for the partial specific property in time and frequency domain.The main goal of this paper is to eliminate the noise by an adaptive neighborhood window of the wavelet domain and focused on selecting a medium-soft threshold function based on wavelet. Simulation results have shown that the modified function improves the denoising effect comparing with the other threshold functions.

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