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An Empirical Study using Line Profile Histogram Approximation of Edge Detection Algorithms

机译:边缘检测算法线轮廓直方图近似的实证研究

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Traditional edge detection algorithms such as Sobel and Prewitt have been used in many edge detection applications. Canny have proved to be quite efficient in detecting thin edges. This paper discusses the efficiency of these three techniques in the detection of the cortical outline of long tubular bone from radiographs images. The method involves the use of line profile histogram approximation (LPHA) algorithm to detect the outline positions. These positions are then used to calculate geometric measurements such as the internal diameter (ID), outer diameter (OD) and the cortical thickness (CT). The accuracy of these measurements are compared with its retrospective manual measurements which was measure using micro calipers by Dr. Lee Cheng Wai. Visually all three techniques performs reasonably well in detecting the bone cortical edge. However in the quantitative measurements, Canny perform quite well in certain cases but not in others. The performance of Sobel and Prewitt are consistent in most cases but less accurate as compared to Canny.
机译:传统的边缘检测算法,如Sobel和Prowitt已经在许多边缘检测应用中使用。在检测薄边缘方面证明了大小写的效率。本文讨论了这三种技术在射线照片图像中检测到长管骨的皮质轮廓的效率。该方法涉及使用线轮廓直方图近似(LPHA)算法来检测轮廓位置。然后使用这些位置来计算诸如内径(ID),外径(OD)和皮质厚度(CT)的几何测量。将这些测量的准确性与其回顾式手动测量进行了比较,这是使用Micro Calipers的衡量标准博士的衡量。在视觉上,所有三种技术在检测骨皮质边缘方面都能合理地进行。然而,在定量测量中,Canny在某些情况下表现得很好,但不在其他情况下表现得很好。在大多数情况下,Sobel和Prewitt的性能在大多数情况下是一致的,但与Canny相比的较低准确性。

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