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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
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
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
Introduction
• Medical Images– Diagnosis, planning, treatment
and education
• Medical Scan– Computed Tomography (CT)
and Magnetic Resonance Imaging (MRI)
– Non-invasive
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
Segmentation• Delineation of regions of interest from an image • Complex process since tumors have different
shapes, sizes, tissue densities, and locations
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).
Limitation in Segmentation Approaches
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
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
Examples of Colorization• User initial paint (Levin et al, Tzeng et al )• Initial color source (Welsh et al 2002)• Color seed (Takahiko et al)
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
Methodology Development
Region of interest selection and colorization
Seed selection for first slice and segmentation
Post-processing and interactive adjustements
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
Colorization
rangeHU
ueHUpixelValP
0
2255
25520.1Re
Blue
PGreen
Pd
25.0255
25.01255
0Re
PBlue
PGreen
d
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
Segmentation
• Color Component
• Distance Component
255
2/1222bbggrr APAPAP
APC
2/122yyxx SPSP
SPD
Segmentation
pRCR
6
123 321 CCCC
ROI
Seed
RMAX
Post-processing
• Morphological Operations
Interactive Adjustements
• 2D Textures– Array of 512x512 sent to the GPU
• Allows for real time visualization of the results
• Allows tweaking of parameters
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
Segmentation tab
• Sliders• Apply all• Plenty of screenshots
Other features
Test Cases Description
• 10 different test cases with different levels of difficulty
• Several runs of each test cases
Results
• Gold Standard– Two radiologists manually segmented the
results• False positive and false negative were
calculated
%100
)(
)(x
RV
RAVAVFP
Results
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.
Results- Cases A
• Low FN and FP because of difference between tumor and healthy tissues
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
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
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
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.
Thank You
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