Contrast Enhancement
Crystal Logan
Mentored by: Dr. Lucia DettoriDr. Jacob Furst
Project Objective
Assist Radiologist in reading images
Enhance the Contrast of Images
“The Big Picture”Explore Contrast Enhancement techniques Linear binning equally divides ranges of
grey levels into binsHistogram Equalization enhances images by
plotting frequency Automatically enhance multiple regions of the
image.
Previous work on Multiple Windows
User selects the number of windows (1-3) on which to apply contrast enhancement
User specifies the grey level ranges for each window to be used
User selects the Contrast Enhancement algorithm to be used
The selected algorithm is applied to the regions
Original image, and the enhance image are displayed
Example of Windows
Example of Windows
Example of Windows
Research Objective
Enhance the Contrast of ImagesExplore Contrast Enhancement techniques Automatically enhance multiple regions of the
image
Expectation Maximization
EM algorithm identifies four Gaussian to be used to partition the histogram of the image in four regions
Parameters: means and standard deviations of the Gaussian curves
The parameters are estimated by likelihood functions
Iterative Process
Expectation Maximization
First Iteration Second Iteration
Copyright © 2001, Andrew W. Moore
Expectation Maximization
Third Iteration fourth Iteration
Copyright © 2001, Andrew W. Moore
Expectation Maximization
fifth Iteration Sixth Iteration
Copyright © 2001, Andrew W. Moore
Expectation Maximization
Copyright © 2001, Andrew W. Moore
Twentieth Iteration
Expectation Maximization Expectation Step:
Sets initial value for the parameter by using kmeans cluster.
Maximization Step:Uses the data from the expectation step to
estimate the parameter, by taking the derivative. Repeat iteration until there is Convergence.
K-means Cluster statistical algorithm k the number of clusters (4 in our case) Find the centroids for the clusters Calculates distance of all elements from
the centroids Group elements from the centroids.
EM Results
Regions Air Water Tissue Bone
0.12 0.39 0.46 0.018
location 799 1019.5 104.7 1234.1
Expectation Maximization
EM Image Histogram & Gaussian:
EM image Histogram & Gaussian
Analysis Graphs
The Gaussian graph are accurately estimating the centroids.
Identification Algorithm gives us a estimate of how much materials are in each region
based on the maximization step.
Iterations Manipulating the iterations in both the K mean and EM algorithm,
resulted in k-mean iterations isn’t crucial, and EM iterations did change one of the Gaussian curves’ amplitude
Future Works Explore CE techniques and put them into
windows by the using the EM Measure the Contrast in the image using
Greedy Algorithms