Farag Aly A, El-Baz Ayman, Gimel'farb Georgy, Falk Robert, El-Ghar Mohamed A, Eldiasty Tarek, Elshazly Salwa
Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY 40292, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):662-70. doi: 10.1007/11866565_81.
To more accurately separate each pulmonary nodule from its background in a low dose computer tomography (LDCT) chest image, two new adaptive probability models of visual appearance of small 2D and large 3D pulmonary nodules are used to control evolution of deformable boundaries. The appearance prior is modeled with a translation and rotation invariant Markov-Gibbs random field of voxel intensities with pairwise interaction analytically identified from a set of training nodules. Appearance of the nodules and their background in a current multi-modal chest image is also represented with a marginal probability distribution of voxel intensities. The nodule appearance model is isolated from the mixed distribution using its close approximation with a linear combination of discrete Gaussians. Experiments with real LDCT chest images confirm high accuracy of the proposed approach.
为了在低剂量计算机断层扫描（LDCT）胸部图像中更准确地将每个肺结节与其背景分离，使用了两种新的小二维和大三维肺结节视觉外观自适应概率模型来控制可变形边界的演变。外观先验通过体素强度的平移和旋转不变马尔可夫-吉布斯随机场建模，其成对相互作用从一组训练结节中解析识别。当前多模态胸部图像中结节及其背景的外观也用体素强度的边际概率分布表示。使用离散高斯的线性组合对结节外观模型进行近似，从而从混合分布中分离出来。对真实LDCT胸部图像的实验证实了该方法的高精度。
