An Interactive Segmentation Approach Using Color Pre- processing Marisol Martinez Escobar Ph.D Candidate Major Professor: Eliot Winer Department 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 First X-ray *Wikipedia 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 HU (air) – HU (bone) Windowing Process –Reduces HU values to a range TissueValue (HU) Fat-90 Water0 Muscle+44 Bone Width Center HU Intensity

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 HU Min HU Max Midpoint RedGreenBlue

Colorization

Segmentation User selects a seed Segmentation is based on distance and color –Tp = pixel threshold, –C = Color component, –D = Distance component –R = search radius

Segmentation Color Component Distance Component

Segmentation ROI Seed R MAX

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 VisualizationSegmentationCollaboration 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

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 GrayscaleColor Test Case# Level FPFNFPFN 1 A B B C

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