Edge detection in prostatic ultrasound images using integrated edge maps
Edge detection in prostatic ultrasound images using integrated edge maps
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DOI：
10.1016/s0041-624x(97)00126-1
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发表时间：
1998-02-01
期刊：
影响因子：
4.2
通讯作者：
Wijkstra, H
中科院分区：
文献类型：
--
作者：
Aarnink, RG;Dev Pathak, S;Wijkstra, H
目的：我们研究了一种算法来检测灰度级转换与多尺度的分辨率，以提高边缘检测和定位在超声图像的prostate.Introduction：我们已经开发了一个非分析算子的前列腺轮廓确定实施最小和最大的过滤器，以识别和定位边缘。我们实现了一种技术，用于改善前列腺超声图像中的边界部分的确定通过调整边缘检测参数信号information.Methods：首先，预滤波器设置和边缘检测参数的影响进行了研究，在测试图像和一个真实的超声图像。然后，局部标准偏差被用来识别更多或更少的均匀区域，这些均匀区域是用粗分辨率过滤的，而具有较大偏差的区域表明发生灰度级过渡，这应该使用较小的过滤器尺寸来保持以改善边缘localis.Results：对具有不同过滤器尺寸的图像的分析表明，随着过滤器尺寸的增加，区域被合并：对于较大的滤波器，不太明显的边缘消失或移位。两个尺度的分辨率导致一个更好的局部化的边缘时，使用较小的过滤器大小的区域增加了本地standard deviation.Conclusions：本文说明了一种边缘检测方法，适合作为预处理步骤，在医学图像的解释。通过使输入参数适应信号信息，可以在来自不同成像模态的图像中应用对象识别。Also.缺点进行了讨论，导致在一个新的应用程序相结合的定位算法，以找到初始轮廓和描绘算法，以改善轮廓的结果。(C)1998年Elsevier Science B.V.
Objective: We investigated an algorithm to detect grey level transitions with multiple scales of resolution to improve edge detection and localisation in ultrasound images of the prostate.Introduction: We had developed a non-analytical operator for prostate contour determination implemented with minimum and maximum filters to identify and locate edges. We implemented a technique for improved determination of boundary parts in prostatic ultrasound images by adjusting the edge detection parameter to signal information.Methods: First the influence of prefilter settings and edge detection parameters is investigated in a test image and a real ultrasound image. Then, local standard deviation is used to identify more or fewer homogeneous regions that are filtered with course resolution, while areas with larger deviation indicate that grey level transitions occur, which should be preserved using smaller filter sizes to improve edge localisation.Results: Analysis of images with different filter sizes indicated that areas are merged for increasing filter sizes: less pronounced edges disappear or displace for larger filters. Two scales of resolution lead to an improved localisation of edges when smaller filter sizes are used in areas with an increased local standard deviation.Conclusions: This paper illustrates an edge detection method suitable as pre-processing step in interpretation of medical images. By adapting input parameters to signal information, object recognition can be applied in images from different imaging modalities. Also. disadvantages are discussed, resulting in a new application combining a localisation algorithm to find the initial contour and a delineation algorithm to improve the outlining of the resulting contour. (C) 1998 Elsevier Science B.V.