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Contrast EnhancementA brief summary of what has been doneThings I would like to explore next
Texture classification Evaluations of segmentation algorithms
Broad goal – The big picture
Manipulate (medical) images to facilitate the radiologists’ job of recognizing features and pathologies in radiological images
Improve the visual quality of an image and automatically “highlight” certain features
Give them a way to focus on subsets of the image that are of interest to them
Contrast is all we “see”
Human eye identifies details by contrasting an object (foreground) and its background
Improve the quality of the image by creating (color) contrast
In our case we are talking about CT scan images with different levels of grey
Contrast enhancement
Take the gray level intensities of an image and proportionally redistribute them
Some mapping is necessary anyway since the images are based on 12 bits of information (gray levels ranges from 0 to 4095) and on these monitors we can only display 8 bits (gray levels from 0 to 255)
Can we do a better job?
Example
Techniques implemented
Linear binningLinearly redistribute the intensities from a
range of 0 - 4095 to a range of 0-255 over a chosen number of bins
Nonlinear binningFirst identify clusters of intensities then use
those to guide the redistribution of gray levels Histogram equalization
Soft tissues or bones?
Radiologist might be interested in only some parts of the image:Soft tissuesLungsBones
Each of these correspond to a different range of grey levels
Local contrast enhancement
Instead of trying to enhance the entire picture, concentrate the enhancing power in the range of intensities you are interested in
Window enhancement Multiple windows enhancement
Where are we now?
Previous students have created an application (C#) that allows the user to select windows and technique and display the enhanced image
Things I’d like to explore
Find an objective way to measure the improvement resulting from the contrast enhancement
See how these techniques perform when using different image modalities beyond CT-scans
Explore additional contrast enhancement techniques
Projects
Contrast Enhancement Texture classification
A brief summary of what has been doneThings I would like to explore next
Evaluations of segmentation algorithms
The big pictures
Given a pre-segmented organ region, can you tell me what it is: kidney, heart etc?
It depends … on its texture Identify image features that give texture
information Find rules that distinguish the texture
features of one organ from another
Texture Classification Process at a glance
Apply filterTo the image
Physician annotatedOrgan/Tissue
TextureDescriptors
Classifier(Decision Tree)
Classification rules for tissue/organs in
CT images
Liver Kidney Bone Spleen Heart
Image Data Set
Organ Qty Image Organ Qty Image
Kidney 223 Aorta 66
Liver 260 IP Fat 59
Spleen 95 Muscle 198
Trabecular Bone 39 SQ Fat 157
Lung 15
Total Images
1112
Step3 – Texture features extraction
Apply Gabor filters to the
image
TextureDescriptors
For example:Mean, standard deviation, energy, entropy etc..
Array of texture descriptors
[T1, T2, T3, …, Tn]
Physician annotatedOrgan/Tissue
Liver
Step4 - Classification
Apply Gabor filters to the
image
TextureDescriptors
Classification rules for tissue/organs in
CT images
The process of identifying a region as part of a class (organ) based on its texture properties.
Decisiontree
Predicts the organ from the values of the texture descriptorsTraining / Testing
Classificationperformance
measures
Physician annotatedOrgan/Tissue
Step5 – Evaluating the classifier
Apply Gabor filters to the
image
TextureDescriptors
Classification rules for tissue/organs in
CT images
Decisiontree
Things I would like to explore
Wedgelet transforsFractal Dimensions
Performancemeasures
Test Gabor texture descriptors on additional images and natural images
Physician annotatedOrgan/Tissue
Projects
Contrast enhancement Texture classification Evaluations of segmentation algorithms
Brief summary of what has been doneThings I would like to explore next
Texture segmentation Given an image, can you tell me
how many organs you have? That was easy enough. Can you
tell which organs they are?
Identifying regions with similar texture Identifying which texture it is to label the
organ
A couple of key questions
Can you do it better by varying a parameter? How do you choose the values of your segmentation parameters?
If it looks better is it really better?
A couple of key questions
Parameter optimization
Performance evaluation
1 3
4 2
0.87 0.56
0.50 0.75
Increasing value of a segmentation parameter
GroundTruth
Regionskey
Machine
Segmentations
How do I decide what the optimal value of the parameter is?
How good a segmentation is it?
The “goodness” metric
A single value that assigns a rating to a particular segmentation based on how well the machine segmented regions “match” the regions in the ground truth images
Region Categories
Ground Truth vs. Machine Segmented Correctly Detected Over Segmented Under Segmented Missed Noise
GT
MS
CORRECTLY DETECTED
OVER SEGMENTED
UNDER SEGMENTED
A Missed region is a GT region that does not participate in any instance of CD, OS, or US
A Noise region is an MS region that does not participate in any instance of CD, OS, or US
Index for each region
The “Goodness” Metric good = Correct Detection Index bad = 1-Correct Detection Index goodness = good-bad*weight
1.0
-1.0
Ceiling = CDind
Floor = 2*CDind-1
Weight Range = CDind-1
How can we use the metric?
Create a set of ground truth mosaic using radiologist-labels images of pure patches of organ tissues
Apply segmentation algorithm Optimize the segmentation parameters using the
metric Apply optimized algorithm to the “real” image
Ground Truth Region key
T=1000; GM= - .94
T=4000; GM= .74
T=3000; GM= .73T=2000; GM= - .02
T=5000; GM= .75 T=6000; GM= .08
Done so far
Used the metric on a block-wise walevet-based segmentation algorithm on some sample mosaic
To be done
Fully test the metric on a wide range of segmentation algorithms
Decouple the various components of the metric and test the individual performance measures instead of the overall score
Extend the metric to measure one region vs background segmentation
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