Enhancement and Intensity Inhomogeneity Correction of Diffusion-Weighted MR Images of Neonatal and Infantile Brain Using Dynamic Stochastic Resonance

2017 ◽  
Vol 37 (4) ◽  
pp. 508-518 ◽  
Author(s):  
Munendra Singh ◽  
Shiru Sharma ◽  
Ashish Verma ◽  
Neeraj Sharma
2013 ◽  
Vol 7 (2) ◽  
pp. 174-184 ◽  
Author(s):  
Rajlaxmi Chouhan ◽  
Rajib Kumar Jha ◽  
Prabir Kumar Biswas

2013 ◽  
Vol 12 (03) ◽  
pp. 1350010 ◽  
Author(s):  
RAJIB KUMAR JHA ◽  
APOORV CHATURVEDI ◽  
RAJLAXMI CHOUHAN

In this paper, a dynamic stochastic resonance (DSR) based watermark detection technique in discrete wavelet transform (DWT) domain is presented. Pseudo random bit sequence having certain seed value is considered as a watermark. Watermark embedding is done by embedding random bits in spread-spectrum fashion to the significant DWT coefficients. Watermark detection is quantitatively characterized by the value of correlation. The performance of watermark detection is improved by DSR which is an iterative process that utilizes the internal noise present in the image or external noise which is added during attacks. Even under various noise attacks, geometrical distortions, image enhancement and compression attacks, the DSR-based random bits detection is observed to give noteworthy improvement over existing watermark detection techniques. DSR-based technique is also found to give better detection performance when compared with the suprathreshold stochastic resonance-based detection technique.


MACRo 2015 ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 79-90 ◽  
Author(s):  
László Lefkovits ◽  
Szidónia Lefkovits ◽  
Mircea-Florin Vaida

AbstractIn automated image processing the intensity inhomogeneity of MR images causes significant errors. In this work we analyze three algorithms with the purpose of intensity inhomogeneity correction. The well-known N3 algorithm is compared to two more recent approaches: a modified level set method, which is able to deal with intensity inhomogeneity and it is, as well, compared to an adaptation of the fuzzy c-means clustering with intensity inhomogeneity compensation techniques. We evaluate the outcomes of these three algorithms with quantitative performance measures. The measurements are done on the bias fields and on the segmented images. We consider normal brain images obtained from the Montreal Simulated Brain Database.


2011 ◽  
Vol 7 (1) ◽  
pp. 119-128 ◽  
Author(s):  
Rajib Kumar Jha ◽  
P. K. Biswas ◽  
S. Shrivastava

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