Membership by distance Assessing Tissue Characterization of Abdominal Organs using Fuzzy C-Means cluster analysis of color- fusion MR images Terrance Weeden1 , Arend Castelein2, Yong Wei, H. Keith Brown 1Department of Basic Sciences, GA Campus Philadelphia College of Osteopathic Medicine, Suwanee, GA 30024 2Department of Computer Science, University of North Georgia, Dahlonega, GA 30597 ABSTRACT DATA Color fusion MRI is being investigated for its value in automatic segmentation of tissues. An existing color fusion MRI data set of the liver, pancreas, and kidney of a normal male volunteer was analyzed both visually and statistically. Automatic tissue segmentation can allow better differentiation of abdominal pathologies, as well as pathologies associated with other organs. My research hypothesis is that fuzzy c-means clustering can be used to quantify the confidence levels of correct classification of renal, pancreatic, and hepatic tissues visualized by the color fusion MRI method. Results from data show that fuzzy c-means clustering can be used to validate the correctness of classification of abdominal tissues that are visualized by color fusion MRI. Initial results show that fuzzy C-means cluster analysis can be used to confirm the correctness of classifications of the kidney, liver, and pancreas using color fusion MRI. The purpose of these analyses was to apply fuzzy C-means to statistically validate the visual identifications of tissue classification in color MRI. Visual analysis of hepatic and pancreatic tissues showed that these organs share similar RGB values despite differences in intensities. This difference is due to certain histological differences between the two organs. Unlike pancreas, hepatic tissue has extensive blood sinusoids , stores iron and has many mitochondria containing cytochrome c. The presence of ferritin and cytochrome c has an effect on the imaging of the liver. This explains why, upon visual analysis, the liver would have a lower intensity or increased color saturation than the pancreas. After measuring the Euclidean distance between cluster centers, it was discovered that correct tissue characterization should not be based on distance alone. Once the standard deviations of all pixels in each cluster were taken into consideration, a more accurate classification was be achieved. With these findings, the next phase of this research will set a goal of providing specific classification of tissue characteristics with confidence levels upon selecting an unknown ROI in color MR images. Fig .1: (above) abdominal color MRI of normal male volunteer. Hepatic tissue (dark red) is located on the far left side of the image. Renal tissue (pink or light red) is found adjacent to the liver. Pancreatic tissue (moderate red) is located toward the center of the MR image. PROJECT OBJECTIVES Fig. 2 (above) abdominal color MRI of same normal male volunteer with selected regions of interest. The following labels represent the different areas of interest within pancreatic (2-03 and 2-04) and renal (3-01 thru 3-09) tissue. The RGB and L*A*B* values were assessed in all of the above regions of interest. Selection of regions of interest on color MR images Visual analysis: assessed RGB color values of hepatic, pancreatic, and renal tissues in a color MR image Statistical analysis: analyzed data from fuzzy C-means clustering analysis to determine if fuzzy C-means can numerically express the confidence levels of the classification of abdominal tissues through Euclidean distance measurements and t-score calculations comparing RGB, L*A*B and A*B* color values. Image Name Number muscle_3_test.jpg (126) Color system R*G*B L*A*B A*B Center [ 67, 31, 34 ] [ 44, 146, 135 ] [ 146, 135 ] Distance Data 108.386 62.444 17.496   40.115 28 5.326 13.693 8.109 5.954 58.77 40.151 5.274 86.609 49.574 13.014 241.747 139.464 36.958 T-score data 183.399 185.834 55.899 74.477 87.466 29.719 17.196 16.577 27.289 75.795 86.163 39.419 207.938 199.08 87.152 266.372 295.6 92.494 Membership by distance Muscle Pancreas Membership by T-score Experiments The samples used in this research are Color MRI images (U.S. Patent No. 5,332,968 – “Magnetic Resonance Imaging Color Composites”) from a preexisting abdominal MR image data set from a normal male volunteer (Data collected under Brenau University’s IRB by Dr. Brown). The MRI data was obtained through a contract between Dr. Brown and the North East Georgia Medical Center during his employment at Brenau University in Gainesville, Georgia. Parameters used for the MRI scanning session were: GE Genesis Signa 1.5 T, Software version 08. Slice plane, position, thickness, spacing, Field of View (FOV), magnification factor, and matrix size were the same for each of several pulse sequences. The resulting series of color MRI images ( 256x256, RGB 24 bit color) of abdominal organs including liver, pancreas, and kidney were used to select specific representative regions of these organs as samples to statistically test the segmentation / characterization of the liver, pancreas and kidney by the fuzzy C-means statistical test. This test originated from L.A. Zadeh, who pioneered the theory of fuzzy c-means clustering. Visual analysis of the images was made to assess the RGB intensity values. Then, the fuzzy C-means clustering method was applied to segment the specified regions of these three organs in the color MRI images. Results show that fuzzy C-means can verify the correctness of segmented tissues. Testing of the levels of confidence of correct tissue classification using the Euclidean distance measurements as well as t-test statistics were performed. ACKNOWLEDGMENTS I would like to personally thank Dr. H. Keith Brown for agreeing to be my mentor. His guidance and support are greatly appreciated in ways that cannot be expressed. I would also like to thank Dr. Yong Wei and Arend P. Castelein from North Georgia College. Without their outstanding expertise of segmentation methods, I would not have sufficient data or knowledge of fuzzy c-means clustering analysis. Dr. Deadmond and Dr. Hardy also provided ample guidance and instruction throughout this project. Table 1: (above) Mean RGB or pixel intensity values for liver, pancreas, and kidney from visual analysis. From the table, it can be deduced that the kidney showed a higher level of intensity for red; thus, hepatic tissue appears dark red on the image. REFERENCES 1. Brown, H. Keith et al. "PC-Based Multiparameter Full-Color Display For Tissue Segmentation In MRI Of Adnexal Masses." Journal Of Computer Assisted Tomography 17.6 (1993): 993-1005. 2. Phillips, W. Eugene II et al. "Neuroradiologic MR Applications With Multiparametric Color Composite Display." Magnetic Resonance Imaging 14.1 (1996): 59-72. Table 2 (above, right): Statistical results of a region of interest (ROI) within muscle. Determining the identity of the organ solely based on the distance from the cluster center may give inaccurate results, as seen above in the “Membership by distance” row. In this example membership based on distance classified this muscle ROI as pancreatic tissue. However, once the distance was measured in terms of standard deviations through the use of t-distribution, the correct identity of this ROI was obtained. The lowest t-score corresponds to a greater likelihood of membership of an “unknown” ROI to a known ROI. 3. Wei, Yong et al. “Brain MRI Image Segmentation using Fuzzy C-Means Clustering.” (2010) 4. Zadeh, L.A. “Fuzzy Sets.” Information and Control 8, 338-353 (1965).