A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection

2017 ◽  
Vol 146 ◽  
pp. 59-68 ◽  
Author(s):  
Rajesh Kumar ◽  
Subodh Srivastava ◽  
Rajeev Srivastava
Keyword(s):  
Cancer Detection ◽  
Fourth Order ◽  
Poisson Noise ◽  
Fuzzy C Means ◽  
Fourth Order Pde

scholarly journals The Improvement of ROF De-noising Model Based on AOS and Fourth-order PDE

2011 ◽  
Vol 15 ◽  
pp. 2778-2782 ◽  
Author(s):  
Yonghong Zhang ◽  
Yang Ding ◽  
Lihua Wang
Keyword(s):  
Fourth Order ◽  
Model Based ◽  
Fourth Order Pde

scholarly journals A Multiplicative Noise Removal Approach Based on Partial Differential Equation Model

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Chen ◽  
Jin-Lin Cai ◽  
Wen-Sheng Chen ◽  
Yan Li
Keyword(s):  
Fourth Order ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
Speckle Noise ◽  
Noise Removal ◽  
Equation Model ◽  
Superior Performance ◽  
Pde Model ◽  
Novel Approach ◽  
Fourth Order Pde

Multiplicative noise, also known as speckle noise, is signal dependent and difficult to remove. Based on a fourth-order PDE model, this paper proposes a novel approach to remove the multiplicative noise on images. In practice, Fourier transform and logarithm strategy are utilized on the noisy image to convert the convolutional noise into additive noise, so that the noise can be removed by using the traditional additive noise removal algorithm in frequency domain. For noise removal, a new fourth-order PDE model is developed, which avoids the blocky effects produced by second-order PDE model and attains better edge-preserve ability. The performance of the proposed method has been evaluated on the images with both additive and multiplicative noise. Compared with some traditional methods, experimental results show that the proposed method obtains superior performance on different PSNR values and visual quality.

Sign in / Sign up

Export Citation Format

Share Document