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![Page 1: An Interactive Segmentation Approach Using Color Pre- processing Marisol Martinez Escobar Ph.D Candidate Major Professor: Eliot Winer Department of Mechanical.](https://reader035.fdocuments.net/reader035/viewer/2022062716/56649e0c5503460f94af4d60/html5/thumbnails/1.jpg)
An Interactive Segmentation Approach
Using Color Pre-processing
Marisol Martinez EscobarPh.D Candidate
Major Professor: Eliot WinerDepartment of Mechanical Engineering &
Human-Computer Interaction
December 9, 2009
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Outline• Introduction • Background
– Segmentation methods– Colorization methods
• Methodology– DICOM colorization method– Segmentation approach
• Results– Statistical analysis of results– Comparison between grayscale & colorization
• Conclusions• Future Work
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Introduction
MRI Hand Scan*University of Exter http://centres.exeter.ac.uk/pmrrc/gallery/hand/hand.html
First X-ray*Wikipedia X-rayhttp://en.wikipedia.org/wiki/X-ray
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Introduction
• Medical Images– Diagnosis, planning, treatment
and education
• Medical Scan– Computed Tomography (CT)
and Magnetic Resonance Imaging (MRI)
– Non-invasive
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Medical Data
• Stored as Hounsfield Units (HU)– Tissue density relative to water– Usually ranges -1000 HU (air) – +1000 HU (bone)
• Windowing Process – Reduces HU values to a 0-255 range
Tissue Value (HU)
Fat -90
Water 0
Muscle +44
Bone +1005
255
0
-1000 +1000
Width
Center
HU
Inte
ns
ity
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Segmentation• Delineation of regions of interest from an image • Complex process since tumors have different
shapes, sizes, tissue densities, and locations
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Segmentation Approches• Classical Methods (Hojjatoleslami et al 1998, Pole
et al, Zhang et al 2001)
• Advanced Methods (Vincken et al 1997, Xu et al 2000, Kaus et al 2004)
• Hybrid Methods (Gibou et al 2005, Atkins et al 1998).
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Limitation in Segmentation Approaches
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Color Segmentation
• Classical techniques (Lin et al), advanced techniques (Chent et al, Verikas et al) Hybrid approaches (Cremers et al)
• Limitations– Not applied for internal tumor segmentation– RGB source files– Mostly applied to non-medical segmentation
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Colorization• Process of adding color to a grayscale image by the use of a computer
– Add color channels to the image from 1 channel to 3 channels– Possible number of colors from 256 to 16 million.– No unique solution
• Adding information can improve segmentation
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Examples of Colorization• User initial paint (Levin et al, Tzeng et al )• Initial color source (Welsh et al 2002)• Color seed (Takahiko et al)
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Research Issues
• Improve the accuracy of tumor segmentation from medical image data using color pre-processing and interactive user inputs.
• To provide an easy to use tool that will aid in the Medical field
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Methodology Development
Region of interest selection and colorization
Seed selection for first slice and segmentation
Post-processing and interactive adjustements
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Colorization
• User selects region of interest• The region of interest determines the HU
range
minmax HUHUHUrange HU Min
HU Max
Midpoint
Red Green Blue255
0 0
0
255
255
0 0
0
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Colorization
rangeHU
ueHUpixelValP
0
2255
25520.1Re
Blue
PGreen
Pd
25.0255
25.01255
0Re
PBlue
PGreen
d
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Segmentation• User selects a seed• Segmentation is based on
distance and color
– Tp = pixel threshold,– C = Color component,– D = Distance component– R = search radius
R
DCTp
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Segmentation
• Color Component
• Distance Component
255
2/1222bbggrr APAPAP
APC
2/122yyxx SPSP
SPD
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Segmentation
pRCR
6
123 321 CCCC
ROI
Seed
RMAX
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Post-processing
• Morphological Operations
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Interactive Adjustements
• 2D Textures– Array of 512x512 sent to the GPU
• Allows for real time visualization of the results
• Allows tweaking of parameters
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Interface Framework
• Open source libraries– DCMTK– OpenGL– VTK– VRJuggler– wxWidgets
Medical Desktop
Visualization Segmentation Collaboration
Transverse, Sagittal, and Coronal 2D Views
Volume Rendering
Pseudo-coloring
Windowing
Connection to Virtual Reality Environment
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Segmentation tab
• Sliders• Apply all• Plenty of screenshots
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Other features
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Test Cases Description
• 10 different test cases with different levels of difficulty
• Several runs of each test cases
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Results
• Gold Standard– Two radiologists manually segmented the
results• False positive and false negative were
calculated
%100
)(
)(x
RV
RAVAVFP
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Results
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Results – Colorization
• Easy cases have low FN and FP because of different tissue densities
• 10 out of the 20 test cases gave false positives of 25% or less, and 10 out of the 20 test runs gave false negatives of 25% or less.
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Results- Cases A
• Low FN and FP because of difference between tumor and healthy tissues
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Results Cases B & C
• Low FN in calcified cases because algorithm selects tumor tissues correctly
• High FN because tumor tissues that vary are not selected
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Comparison Grayscale vs. Color• Same test cases • FP of up to 52% on the easy cases up to 284% on the difficult
cases• FN of up to 14% on the easy cases and up to 99% on the
difficult cases.• Colorization prior to segmentation yields better results
Grayscale Color
Test Case#
Level
FP FN FP FN
1A
21.8807 14.255 11.0837 14.0453
5B
23.6672 93.077 40.3981 31.7252
6B
224.641 99.545 18.8161 30.1461
7C
19.2508 92.218 5.9099 57.6397
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Summary Results
• Adding color to the original HU values improves segmentation– Half of the test cases show less than 25% FP
and FN for a simple thresholding technique – Same grayscale methods show up to 284% FP
and 99% FN
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Future Work
• Different and more complex segmentation algorithms using color information
• Different colorization methods • Shaders to increase the speed of the results• Improve the user interface.
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Thank You