School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, No. 580, Jungong Road, Yangpu District, Shanghai City, 200093, China.
Med Biol Eng Comput. 2019 Aug;57(8):1629-1643. doi: 10.1007/s11517-019-01985-0. Epub 2019 May 8.
In the field of dental image processing and analysis, automatic segmentation results of dental hard tissue can provide a useful reference for the clinical diagnosis and treatment process. However, the segmentation accuracy is greatly affected due to the limitation of imaging conditions in the oral environment, as well as the complexity of dental hard tissue topology. To further improve the precision of dental hard tissue segmentation, a novel algorithm was presented by using the sparse representation-based classifier and mathematical morphology operations. First, the captured dental image was preprocessed to eliminate the impact of imbalance local illumination. Then, the preliminary dental hard tissue areas were calculated as the initial marker regions based on color characteristics analysis, and the sparse representation-based classifier was applied sequentially to optimize the initial marker regions combined with certain morphological operations. Finally, a modified marker-controlled watershed transform was employed to segment dental hard tissue regions on the basis of the optimized marker regions, and the final results were obtained after homogeneous region merging. The experimental results show that our method has better adaptability and robustness than existing state-of-the-art methods. Graphical abstract.
在口腔医学图像处理与分析领域，牙齿硬组织的自动分割结果可以为临床诊断和治疗过程提供有益的参考。然而，由于口腔环境成像条件的限制以及牙齿硬组织拓扑结构的复杂性，分割的准确性受到了很大的影响。为了进一步提高牙齿硬组织分割的精度，提出了一种基于稀疏表示分类器和数学形态学运算的新算法。首先，对采集到的牙齿图像进行预处理，以消除局部光照不平衡的影响。然后，基于颜色特征分析计算初步的牙齿硬组织区域作为初始标记区域，并应用基于稀疏表示的分类器结合某些形态学运算来优化初始标记区域。最后，基于优化后的标记区域，采用改进的标记控制分水岭变换来分割牙齿硬组织区域，经过均匀区域合并后得到最终结果。实验结果表明，与现有的先进方法相比，我们的方法具有更好的适应性和鲁棒性。
