Segmentation of 3D microPET Images of the Rat Brain by Hybrid GMM and KDE Tai-Been Chen Department of Medical Imaging and Radiological Science, I-Shou University, Taiwan 2013/08/24

Outline Bio-Medical Image: PET & microPET Statistical Methods Application – The hybrid method combining the Gaussian mixture model (HGMM) with kernel density estimation (KDE). Results Conclusion 2

Bio-Medical Image: PET and microPET The biologically active molecule chosen for PET is FDG, an analogue of glucose, the concentrations of tracer imaged then give tissue metabolic activity, in terms of regional glucose uptake. Use of this tracer to explore the possibility of cancer metastasis (i.e., spreading to other sites) results in the most common type of PET scan in standard medical care (90% of current scans). However, on a minority basis, many other radiotracers are used in PET to image the tissue concentration of many other types of molecules of interest. Cited from 3

Clinical Applications of PET/CT 4 Brain Scan [Ref1]Ref Myocardial Perfusion [Ref2]Ref Brain Fusion [ Ref 5 ] Ref Metastatic lesion [Ref3, Ref4]Ref Cardiac Fusion [ Ref 6 ] Ref

Question: How to Segmentation of 3D microPET Images of the Rat Brain Source: 18F-FDG microPET images reconstructed by OSEM (or MLEEM). Data volume is 256 x 256 x 63 (float type). Motivation: The 3D VR of the cerebral cortex was displayed. The segmented map can provides a clearer boundary of the cerebral cortex. Purpose: It is useful to estimate the metabolic rates and tumor volume inside body. 5 3D VR for Rat Brain

The Answer is Applied the Hybrid Gaussian Mixture Method with Kernel Density Estimation This work segments microPET images using a novel hybrid method combining the Gaussian mixture model with kernel density estimation. Segmentation is crucial for registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. 6

The KDE Approach 7 The KDE is utilized to determine the number of clusters used in the GMM and to generate initialized values.

The K-means Algorithm I xyz represents intensity at the xyz th voxel of one set of 3D images. xy in one slice is sized 256×256 pixels, and the number of slices (z) is 63. The k th centric of a cluster and must be initialized, where k = 1, 2,…, K. D k is the distance between I xyz and the k th centric of a cluster. 8

The K-means Algorithm 9

The Hybrid Gaussian Mixtures Model 10

The EM Algorithm of the GMM Step1: Set initial parameters Φ (old). Step2: Update parameters using above Eqs.. Step3: If log L in (Φ (new) ) − log L in (Φ (old) ) < tolerance, then iteration stops; otherwise, return to Step 2 and use the new parameter values. In this work, tolerance is

The Algorithm of Segmentation Scheme Combining the GMM and KDE 12

The Similarity Test Used in Simulated Phantom 13 This simulation study evaluates the ratio of Tanimoto similarity for the proposed segmentation approach.

Study in Simulated Phantom K-means (2)K-means (3)K-means (4) GMM+KDE (2)GMM+KDE (3)GMM+KDE (4) 14

Similarity Test 15 Cluster (K) Tanimoto Similarity KDE+GMMK-Means

Study in Rat Brain 16 7 Clusters

Comparison Signal-to-Noise Ratio (SNR) between K-Means and KDE+GMM 17 K=5 KDE+GMMK-MeansSNR MeanSDMeanSDKDE+GMMK-Means K= K=

Comparison 3D VR among K-means, Original Image, and KDE+GMM 18

Conclusion The proposed method can be applied to evaluate the distribution of an imaging tracer, analyze images quantitatively, determine tumor size, and estimate volumes of ROI for future dynamic 3D microPET studies. Hybrid the GMM and KDE can also automatic to segment rat brain images. The GMM+KDE might apply to other images, such as SPECT, MRI, CT,..,etc. 19

The Future An integrated project union together with multidisciplinary – Imaging/Images Processing – Information Science and Computer Science – Bioinformatics and Modeling Integrated Medical Information and Biological Images for Personal Medicine. 20

Discussion A good question generated from medical images or issues. The principle of medical imaging and images. The tools of image processing. The using of statistical methods. Cooperation with medical physicians. Multidisciplinary research. 21

Acknowledgement E-DA Hospital, Kaohsiung. Chang Gung Memorial Hospital, Kaohsiung Medical Center. All of cooperators including students, professors and medical physicians. 22

END Thanks for your attention 23