AdvisorStudent Dr. Jia Li Shaojun Liu Dept. of Computer Science and Engineering, Oakland University Automatic 3D Image Segmentation of Internal Lung Structures Image segmentation plays an important role in image processing applications, such as computer aided diagnose (CAD), automatic classification, and computer aided surgery. It becomes more challenging on human lungs because of the complex topologies of the internal structures, such as the bronchial tree. As the internal structures vary with different diseases, like the nodule and tumor, accurate 3D information of the internal structures is helpful to doctors for early diagnose and treatment. Level set is a popular method in this area and many approaches have been proposed for various improvement purposes. For example, as the gradient contains noise, image intensity is used to assist segmentation in the Expectation- Maximization (EM) approach. However, the result is influenced by initialization and global optimum is not guaranteed. We combine the intensity and gradient to depress the gradient noise and make the result more stable. We test our solution on a series of CT lung scans downloaded from the National Cancer Institute. The user only needs to specify a box containing the internal structures, or the region of interest (ROI). Using the ROI with 290*380*62 voxels as the initial front surface, we get the segmentation result as shown by 2D contours on XY plane in Fig. 2. To visualize the segmentation result, we reconstruct the internal structures as shown in Fig. 3. The thin structures like bronchia and fine surface details are well captured. Note that some bronchia are not connected to the major bronchial tree in this part of lungs, not resulted from the segmentation.  1. Abstract This poster presents a new solution for automatic 3D image segmentation of internal lung structures. We combine the gradient and intensity to depress the noise and make level set based segmentation algorithm more stable. The experimental results show that our solution gives good results on the public lung image data.  2. Introduction  3. Segmentation We use a mixed Gaussian distribution model to estimate the intensity distribution. Upper left image in Fig. 1 gives one example of the estimation result of the intensity histogram of a 3D image. By comparing the posterior probability of the intensity of inside and outside regions, we get the segmentation result by intensity method, shown as the upper middle image. It resembles at large the gradient of the original image. However, a different initial in bottom left image gives segmentation result in bottom middle image in Fig. 1, which changes obviously from the previous one. To reduce this instability, we combine the gradient with the intensity in the speed function of level set method. It gives the results shown in upper and bottom right images in Fig. 1, which does not change much.  5. Conclusion and Discussion  4. Experimental Results Fig. 1 Segmentation results of intensity method and proposed method. Fig. 2 3D segmentation of internal lung structures shown by 2D contours on 4 individual slices at z=4, 16, 26, 60. Fig. 3 3D internal lung structures reconstructed from segmentation results. From the bottom left image in Fig. 2 and the left image in Fig. 3, it is easy to detect a suspicious structure in the left lung. We can re-assign the ROI to segment or only reconstruct the structure. Right image in Fig. 3 shows its 3D surface and Fig. 4 shows its 2D contours. It is a nodule with many connections. Our method draws the complete outline of the nodule which encloses the core part marked by the physicians. Fig. 4 A Nodule captured by the proposed 3D segmentation method. In this paper we provide an automatic solution for 3D image segmentation of internal lung structures. It combines the gradient and intensity to filter out the noise and makes the segmentation result more stable. Actually with small modifications our solution can be used on other organs, such as brain, heart and liver as well. In the future we also want to (1)segment specific structures, like nodule; (2)and automatic classification of malignant and benign nodules based on the extracted features.