Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
IEEE Trans Biomed Eng. 2011 Mar;58(3):567-73. doi: 10.1109/TBME.2010.2091129. Epub 2010 Nov 9.
Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties.
在噪声图像中找到正确的边界仍然是一项困难的任务。本文介绍了一种新的边缘跟踪技术，用于噪声图像中的边界检测。通过将该技术应用于各种类型的医学图像，展示了其应用。我们提出的技术可以使用强度梯度的信息通过向量图像模型和边缘图的纹理梯度来检测噪声图像中物体的边界。已经测试了该技术的性能和鲁棒性，以分割包括超声图像中的前列腺、心脏磁共振（MR）图像中的左心室、心血管 MR 图像中的主动脉和计算机断层扫描图像中的膝关节在内的合成噪声图像和医学图像中的物体。我们将提出的分割技术与主动轮廓模型（ACM）、测地线主动轮廓模型、无边缘主动轮廓、梯度向量流蛇模型和基于矢量场卷积的 ACM 进行比较，使用熟练医生的意见作为地面真实。结果表明，我们的技术表现非常出色，性能优于经典轮廓模型。该方法具有鲁棒性，适用于各种噪声图像，无需事先了解噪声特性。
