Contrast enhancement of computed tomography images by adaptive histogram equalization-application for improved ischemic stroke detection

2012 ◽  
Vol 22 (3) ◽  
pp. 153-160 ◽  
Author(s):  
T. L. Tan ◽  
K. S Sim ◽  
C. P. Tso ◽  
A. K. Chong
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Adaptive Histogram Equalization ◽  
Computed Tomography Images

An Optimization of System for Automatic Recognition of Ischemic Stroke Areas in Computed Tomography Images

Informatica ◽  
2007 ◽  
Vol 18 (4) ◽  
pp. 603-614
Author(s):  
Darius Grigaitis ◽  
Vaida Bartkutė ◽  
Leonidas Sakalauskas
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Automatic Recognition ◽  
Computed Tomography Images

scholarly journals Adaptive Histogram Equalization For Automatic Contrast Enhancement Of Medical Images

1986 ◽  
Author(s):  
Stephen M. Pizer ◽  
John D. Austin ◽  
John R. Perry. ◽  
Hal D. Safrit ◽  
John B. Zimmerman
Keyword(s):  
Contrast Enhancement ◽  
Medical Images ◽  
Histogram Equalization ◽  
Adaptive Histogram Equalization

scholarly journals Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Shibin Wu ◽  
Shaode Yu ◽  
Yuhan Yang ◽  
Yaoqin Xie
Keyword(s):  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Multiscale Analysis ◽  
Histogram Equalization ◽  
Mammographic Images ◽  
Adaptive Histogram Equalization ◽  
Gaussian Pyramid ◽  
Low Pass ◽  
Multiscale Transform ◽  
Improvement Index

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

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