Edge-guided boundary delineation in prostate ultrasound images

被引:126
作者
Pathak, SD
Chalana, V
Haynor, DR
Kim, Y [1 ]
机构
[1] Univ Washington, Dept Bioengn, Seattle, WA 98195 USA
[2] Univ Washington, Dept Elect Engn, Seattle, WA 98195 USA
[3] Univ Washington, Dept Radiol, Seattle, WA 98195 USA
[4] MathSoft Inc, Seattle, WA 98109 USA
关键词
edge detection; edge guidance; graduated nonconvexity; prostate; segmentation validation; sticks; weak membrane fitting;
D O I
10.1109/42.897813
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Accurate detection of prostate boundaries is required in many diagnostic and treatment procedures for prostate disease, In this paper, a new paradigm for guided edge delineation is described, which involves presenting automatically detected prostate edges as a visual guide to the observer, followed by manual editing. This approach enables robust delineation of the prostate boundaries, making it suitable for routine clinical use. The edge-detection algorithm is comprised of three stages. An algorithm called sticks is used to enhance contrast and at the same time reduce speckle in the transrectal ultrasound prostate image. The resulting image is further smoothed using an anisotropic diffusion filter. In the third stage, some basic prior knowledge of the prostate, such as shape and echo pattern, is used to detect the most probable edges describing the prostate. Finally, patient specific anatomic information is integrated during manual linking of the detected edges. The algorithm was tested on 125 images from 16 patients. The performance of the algorithm was statistically evaluated by employing five expert observers. Based on this study, we found that consistency in prostate delineation increases when automatically detected edges are used as visual guide during outlining, while the accuracy of the detected edges was found to be at least as good as those of the human observers, The use of edge guidance for boundary delineation call also be extended to other applications in medical imaging where poor contrast in the images and the complexity in the anatomy limit the clinical usability of fully automatic edge-detection techniques.
引用
收藏
页码:1211 / 1219
页数:9
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