Jiang T, Merickel M B
University of Virginia, Charlottesville 22908.
Comput Med Imaging Graph. 1989 Sep-Oct;13(5):369-82. doi: 10.1016/0895-6111(89)90224-3.
Automated identification and boundary extraction of blobs in "real world" imagery is a difficult task because the boundaries are so irregular that there is often insufficient a priori information describing these boundaries and traditional methods fail. This paper has proposed a progressive segmentation approach and a boundary estimation method to identify the blobs and to yield an accurate description of its boundary. The multiresolution image processing technique is incorporated into the whole work. This work has been applied to the problem of identifying and extracting the boundaries of major vessels (e.g., the aorta) in Magnetic Resonance (MR) imagery and the results are satisfactory. A Laplacian of Gaussian (LOG) operator is utilized as a spot detector to locate the approximate position of the blob of interest. A subimage centered on this approximate position is extracted to eliminate unwanted portions of the image and facilitate further processing. A histogram pyramid is created for the subimage histogram for automated determination of the threshold in the noisy histogram. A shrink-expand operation is then employed to reduced noise and undesired structures in the subimage. The rough and irregular boundary of the blob of interest obtained by thresholding is reparameterized into polar coordinates to create a Fourier descriptor representation of the boundary. Then, the discrete Fourier transform is applied to the reparameterized 1-D discrete curve to permit appropriate smoothing, as required, in frequency space. Finally, the boundary estimation is completed by taking the inverse Fourier transform to reconstruct the boundary of interest.
在“真实世界”图像中自动识别斑点并提取其边界是一项艰巨的任务，因为边界非常不规则，通常缺乏足够的先验信息来描述这些边界，传统方法也会失效。本文提出了一种渐进分割方法和一种边界估计方法，用于识别斑点并准确描述其边界。多分辨率图像处理技术被应用于整个工作中。这项工作已应用于磁共振（MR）图像中主要血管（如主动脉）的识别和边界提取问题，结果令人满意。使用高斯-拉普拉斯（LOG）算子作为斑点检测器来定位感兴趣斑点的大致位置。提取以该大致位置为中心的子图像，以消除图像中不需要的部分并便于进一步处理。为子图像直方图创建直方图金字塔，以便自动确定噪声直方图中的阈值。然后采用收缩-扩展操作来减少子图像中的噪声和不需要的结构。通过阈值处理得到的感兴趣斑点的粗糙且不规则的边界被重新参数化为极坐标，以创建边界的傅里叶描述符表示。然后，将离散傅里叶变换应用于重新参数化的一维离散曲线，以便在频率空间中根据需要进行适当的平滑处理。最后，通过进行傅里叶逆变换来重建感兴趣的边界，从而完成边界估计。
