A dualistic sub-image histogram equalization based enhancement and segmentation techniques for medical images

Author(s):  
K. Raj Mohan ◽  
G. Thirugnanam
Author(s):  
Ashraf Osman Ibrahim ◽  
◽  
Ali Ahmed ◽  
Anik Hanifatul Azizah ◽  
Saima Anwar Lashar ◽  
...  

2007 ◽  
Vol 107 (1-2) ◽  
pp. 108-122 ◽  
Author(s):  
Nikoletta Bassiou ◽  
Constantine Kotropoulos

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Lei Zeng ◽  
Bin Yan ◽  
Weidong Wang

Cone beam computed tomography (CBCT) is a new detection method for 3D nondestructive testing of printed circuit boards (PCBs). However, the obtained 3D image of PCBs exhibits low contrast because of several factors, such as the occurrence of metal artifacts and beam hardening, during the process of CBCT imaging. Histogram equalization (HE) algorithms cannot effectively extend the gray difference between a substrate and a metal in 3D CT images of PCBs, and the reinforcing effects are insignificant. To address this shortcoming, this study proposes an image enhancement algorithm based on gray and its distance double-weighting HE. Considering the characteristics of 3D CT images of PCBs, the proposed algorithm uses gray and its distance double-weighting strategy to change the form of the original image histogram distribution, suppresses the grayscale of a nonmetallic substrate, and expands the grayscale of wires and other metals. The proposed algorithm also enhances the gray difference between a substrate and a metal and highlights metallic materials. The proposed algorithm can enhance the gray value of wires and other metals in 3D CT images of PCBs. It applies enhancement strategies of changing gray and its distance double-weighting mechanism to adapt to this particular purpose. The flexibility and advantages of the proposed algorithm are confirmed by analyses and experimental results.


1986 ◽  
Author(s):  
Stephen M. Pizer ◽  
John D. Austin ◽  
John R. Perry. ◽  
Hal D. Safrit ◽  
John B. Zimmerman

2014 ◽  
Vol 543-547 ◽  
pp. 2788-2791
Author(s):  
Xiang Hua Hou ◽  
Hong Hai Liu

In face recognition system, the purpose of gray pretreatment is to denoise and enhance the image. The traditional linear or nonlinear denoising algorithm can bring edge loss, which make it difficulty for the subsequent image segmentation or matching. Although Alpha filter can bring the minimum loss of image edge, the size of filtering window cannot be adaptively changed according to the noise. The Alpha filter is improved on the basis that the information entropy can reflect the noise strength to some degrees. The single pixel entropy in neighborhood is compared with the information entropy average and then the noise infection of neighborhood pixel is determined. Moreover, according to the noise infection, the window size is adaptively adjusted to filter. The results show that the loss of image edge obviously reduces. Because the image size is fixed, we can calculate the integration of normalized image according to cumulative distribution function of the image. Therefore, the image histogram equalization is derived and the image gray is transformed to get the enhanced image. Finally, the results show that the face image after improved gray pretreatment can well ensure the image edge integrity and the face recognition effect is improved by edge feature.


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