Adaptive filtering for medical image based on 3-order tensor field

2012 ◽  
Author(s):  
Ping Zhang ◽  
Liqun Gao ◽  
Bin Fu ◽  
Zhaohua Cui ◽  
Xiaoyou Shan
Keyword(s):  
Adaptive Filtering ◽  
Medical Image ◽  
Tensor Field ◽  
Order Tensor

A Medical Image Segmentation Method Fusing Anisotropic Diffusion Model

2011 ◽  
Vol 268-270 ◽  
pp. 1121-1126
Author(s):  
Meng Meng Zhang ◽  
Ling Ma ◽  
Zhi Hui Yang ◽  
Yang Yang ◽  
Hui Hui Bai
Keyword(s):  
Image Segmentation ◽  
Diffusion Equation ◽  
Adaptive Filtering ◽  
Anisotropic Diffusion ◽  
Medical Image ◽  
Structural Similarity ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Similarity Algorithm ◽  
Anisotropic Diffusion Equation

The denoising principal of the anisotropic diffusion equation is studied. Adaptive filtering of image is realized by combining the improved image structural similarity algorithm and the anisotropic diffusion equation. This algorithm is applied to medical image segmentation. Experimental results show that the improved algorithm has good robustness and advantages in the application of adaptive medical image filtering and segmentation.

scholarly journals Regularized positive-definite fourth order tensor field estimation from DW-MRI

NeuroImage ◽  
2009 ◽  
Vol 45 (1) ◽  
pp. S153-S162 ◽  
Author(s):  
Angelos Barmpoutis ◽  
Min Sig Hwang ◽  
Dena Howland ◽  
John R. Forder ◽  
Baba C. Vemuri
Keyword(s):  
Fourth Order ◽  
Tensor Field ◽  
Positive Definite ◽  
Order Tensor ◽  
Field Estimation ◽  
Fourth Order Tensor

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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