1. MRI Image Segmentation for Brain Injury Quantification Lindsay Kulkin BRITE REU 2009 Advisor: Bir Bhanu August 20, 2009

2. Overview Background ◦ Stroke Diagnosis ◦ Forms of Image Segmentation Process ◦ Gradient Relaxation Algorithm ◦ Connected Components ◦ K-Means Clustering Algorithm Results Conclusions ◦ Other ways to apply these forms of analysis

3. Background What is a stroke? Types  Ischemic  Hemorrhagic Causes  Thrombosis*  Embolism  Systemic Hypoperfusion Diagnosis  Computed Tomography (CT) scan  Magnetic Resonance Imaging (MRI) *Thrombosis occurs when a blood clot (known as a thrombus) forms within the blood vessel and does not break free.

4. Image Segmentation Manual SegmentationAutomatic Segmentation Time consuming and often inaccurate Can vary over 30% from person to person and can take hours per patient A faster and more accurate process Repeatable and would take a matter of minutes Original Image Manual Segmentation Automatic Segmentation

5. Gradient Relaxation Algorithm Find the maximum kept constant (ρ imax ) and the ρ i constant for all pixels Find the initial assignment of probability (P i ) and the mean neighborhood probability (q i ) Construct a threshold image* Based on the valley of the histogram, segment the first iteration and create a binary image (threshold value = 130)

6. Gradient Relaxation Algorithm Images provided by the Loma Linda University Medical Center, 2007 Original Image First IterationBinary Image With each iteration, each new pixel value is determined based on the probability of its own pixel value as well its neighboring pixels (3x3 window) While the program runs until it terminates, the threshold is automatically selected based on the histogram of the first iteration

7. Connected Components Analysis Mask 11022203 11020203 11110003 00000003 44440503 00040503 66040003 66040333 Pixel labels for Binary Image Preliminary Scan Final Image Connected components identifies contiguous sets of connected pixels and is reapplied until the image cannot be segmented any further

8. Connected Components Analysis Connected Components Threshold ImageInverted Image Total pixels excluding background: 11,610 White: 10,940 (94.2%) Large Injury: 502 (4.32%) Small Injury: 168 (1.45%)

9. K-Means Clustering Algorithm Isolate each component by setting all other pixels to zero Select a k value as the initial cluster centers and find the distance between each pixel and each cluster center Find the mean value of each cluster center For all pixels, assign each pixel to its closest cluster center. Find the mean value of each cluster center until the cluster centers do not change Original Image

10. K-Means Clustering Algorithm Total pixels excluding background: 10,653 Yellow: 602 (5.7%) Red: 5740 (53.9%) Blue: 4311 (40.5%) Total pixels excluding background: 502 Yellow: 272 (54.2%) Aqua: 124 (24.7%) Blue: 106 (21.2%) Total pixels excluding background: 168 Yellow: 89 (53%) Aqua: 79 (47%)

11. Data Analysis Form of Analysis Total Area (Pixels) Damaged Area (Pixels) Percent Damaged Gradient Relaxation11,6106705.77 K-Means Clustering10,6536025.65 Manual Segmentation 11,6107596.54 S.D.0.48 Mean5.99 Gradient Relaxation Algorithm Manual Segmentation K-Means Clustering Algorithm

12. Conclusions Automatic segmentations vs. manual segmentation Both are effective and consistent Automatic segmentation is much faster These approaches can be applied to each MRI slice and the volume of injury can be obtained In the future, other forms of brain injury can be analyzed through the use of either: The gradient relaxation algorithm/connect components analysis K-Means Clustering algorithm

13. Acknowledgments I would like to thank: Professor Bir Bhanu for his guidance My graduate student advisor Benjamin X. Guan, as well as Angello Pozo and Giovanni DeNina The Center for Research in Intelligent Systems (CRIS) Jun Wang for this opportunity and for his support Loma Linda University Medical Center for providing the MRI images

14. Questions?