Dehkordi Maryam Taghizadeh, Jalalat Morteza, Sadri Saeed, Doosthoseini Alimohamad, Ahmadzadeh Mohammad Reza, Amirfattahi Rasoul
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran ; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
J Med Signals Sens. 2014 Apr;4(2):150-7.
Vessel extraction is a critical task in clinical practice. In this paper, we propose a new approach for vessel extraction using an active contour model by defining a novel vesselness-based term, based on accurate analysis of the vessel structure in the image. To achieve the novel term, a simple and fast directional filter bank is proposed, which does not employ down sampling and resampling used in earlier versions of directional filter banks. The proposed model not only preserves the performance of the existing models on images with intensity inhomogeneity, but also overcomes their inability both to segment low contrast vessels and to omit non-vessel structures. Experimental results for synthetic images and coronary X-ray angiograms show desirable performance of our model.
血管提取是临床实践中的一项关键任务。在本文中，我们基于对图像中血管结构的精确分析，通过定义一个基于新的血管性术语，提出了一种使用主动轮廓模型进行血管提取的新方法。为了实现这个新术语，我们提出了一种简单快速的方向滤波器组，它不采用早期版本方向滤波器组中使用的下采样和重采样。所提出的模型不仅在具有强度不均匀性的图像上保持了现有模型的性能，而且克服了它们在分割低对比度血管和忽略非血管结构方面的不足。合成图像和冠状动脉X射线血管造影的实验结果表明了我们模型的良好性能。
