一种低对比度CT图像的血管分割方法

摘要

CT图像血管分割技术在疾病的诊断，手术规划等许多实际应用中发挥着重要的作用。由于个体性差异和成像设备的限制，造影后的血管通常存在对比度低和噪声高的缺陷。针对该数据特点提出了一套分割方法，首先采用直方图对图像进行预处理，以增强血管和周围区域的对比度；其次，改进 Hessian 矩阵血管增强的判别方法，使其对细小和模糊的管状结构更加敏感；最后，采用区域生长算法对增强后的数据进行血管提取，获得血管分支较丰富的分割数据。实验证明本文的分割方法可以准确地实现血管分割，有效地避免了误分割，具有较好的鲁棒性。%Vascular segmentation based on CT images plays an important role in many practical applications, such as disease diagnosis, surgical planning and so on. Due to limitations of individual differences and image forming apparatus, angiographic images still remain low contrast and strong noise. The paper provides a method of vessel segmentation, which first do the image preprocessing using histogram, then improve vesselness function of the vessel enhancement using Hessian matrix to make it more sensitive to small and fuzzy tubular structures, finally region growing algorithm is employed to extract the richer in vessel branching. The experiments proved the segmentation method can be achieved vessel segmentation accurately. It can avoid the error segmentation effectively and has a better robustness.