An efficient spatial domain fusion scheme for multifocus images using statistical properties of neighborhood

Author(s):  
Parul Shah ◽  
Shabbir N. Merchant ◽  
Uday B. Desai
Author(s):  
Alka Srivastava ◽  
Ashwani Kumar Aggarwal

Nowadays, there are a lot of medical images and their numbers are increasing day by day. These medical images are stored in the large database. To minimize the redundancy and optimize the storage capacity of images, medical image fusion is used. The main aim of medical image fusion is to combine complementary information from multiple imaging modalities (e.g. CT, MRI, PET, etc.) of the same scene. After performing medical image fusion, the resultant image is more informative and suitable for patient diagnosis. There are some fusion techniques which are described in this chapter to obtain fused image. This chapter presents two approaches to image fusion, namely spatial domain Fusion technique and transforms domain Fusion technique. This chapter describes Techniques such as Principal Component Analysis which is spatial domain technique and Discrete Wavelet Transform and Stationary Wavelet Transform which are Transform domain techniques. Performance metrics are implemented to evaluate the performance of image fusion algorithm.


2014 ◽  
Vol 1044-1045 ◽  
pp. 1186-1189 ◽  
Author(s):  
Bing Wen Chen ◽  
Shi Long Liu

In order to improve the accuracy and stability of infrared target detection, a novel moving target detection approach based on temporal-spatial domain fusion is presented. A multi-level spatial-temporal median filter is utilized to extract the background frame, with which the background clutters are suppressed by using the background subtraction technique. Then a local weighted operator is applied to enhance the targets. Lastly, the otsu thresholding algorithm is utilized to detect the targets. Experimental results demonstrate that the proposed approach is capable of detecting infrared moving targets effectively for F1 measurement up to 92.8%.


1982 ◽  
Vol 43 (4) ◽  
pp. 585-589 ◽  
Author(s):  
M. N. Bussac ◽  
C. Meunier

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.


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