Chen Ying-Cheng, Chen Yung-Chang, Chiang Ann-Shyn
Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan, ROC.
Comput Methods Programs Biomed. 2008 Mar;89(3):239-47. doi: 10.1016/j.cmpb.2007.11.007.
High quality 3D visualization of anatomic structures is necessary for many applications. The anatomic structures first need to be segmented. A variety of segmentation algorithms have been developed for this purpose. For confocal microscopy images, the noise introduced during the specimen preparation process, such as the procedure of penetration or staining, may cause images to be of low contrast in some regions. This property will make segmentation difficult. Also, the segmented structures may have rugged surfaces in 3D visualization. In this paper, we present a hybrid method that is suitable for segmentation of confocal microscopy images. A rough segmentation result is obtained from the atlas-based segmentation via affine registration. The boundaries of the segmentation result are close to the object boundaries, and are regarded as the initial contours of the active contour models. After convergence of the snake algorithm, the resulting contours in regions of low contrast are locally refined by parametric bicubic surfaces to alleviate the problem of incorrect convergence. The proposed method increases the accuracy of the snake algorithm because of better initial contours. Besides, it can provide smoother segmented results in 3D visualization.
高质量的解剖结构三维可视化对于许多应用来说是必要的。首先需要对解剖结构进行分割。为此已经开发了多种分割算法。对于共聚焦显微镜图像，在标本制备过程中引入的噪声，如穿透或染色过程，可能会导致图像在某些区域对比度较低。这种特性会使分割变得困难。此外，在三维可视化中，分割后的结构可能具有粗糙的表面。在本文中，我们提出了一种适用于共聚焦显微镜图像分割的混合方法。通过仿射配准从基于图谱的分割中获得一个粗略的分割结果。分割结果的边界接近物体边界，并被视为活动轮廓模型的初始轮廓。在蛇形算法收敛后，通过参数双三次曲面在低对比度区域对所得轮廓进行局部细化，以缓解不正确收敛的问题。由于初始轮廓更好，所提出的方法提高了蛇形算法的准确性。此外，它可以在三维可视化中提供更平滑的分割结果。
