초음파 전립선 영상에서 전립선 경계 방향 특징을 이용한 전립선 경계 추출
Extracting The Prostate Boundary Using Direction Features of Prostate Boundary On Ultrasound Prostate Image
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초록
Traditionally, in the hospital the doctors saw the TRUS images by their eyes and manually segmented the boundary between the prostate and nonprostate. But the manually segmenting process not only needed too much time but also had different boundaries according to the doctor. To cope the problems, some automatic segmentations of the prostate have been studied to generate the constant segmentation results and get the belief from patients. Besides, on detecting the boundary, the ones in the middle of all images are easy to find the boundary but the base and apex of the images are hard to do it since there are lots of uncertain boundary. Accurate detection of prostate boundaries is a challenging and difficult task due to weak prostate boundaries, speckle noises and the short range of gray levels. In this paper, we propose the method that extracts a prostate boundary using features of its directions on prostate image. As a result of our experiments, it shows that the boundary never falls short of the existing methods or human expert's segmentation. And also, its searching speed is too fast because the method searches a smaller area that other methods.
Abstract
Traditionally, in the hospital the doctors saw the TRUS images by their eyes and manually segmented the boundary between the prostate and nonprostate. But the manually segmenting process not only needed too much time but also had different boundaries according to the doctor. To cope the problems, some automatic segmentations of the prostate have been studied to generate the constant segmentation results and get the belief from patients. Besides, on detecting the boundary, the ones in the middle of all images are easy to find the boundary but the base and apex of the images are hard to do it since there are lots of uncertain boundary. Accurate detection of prostate boundaries is a challenging and difficult task due to weak prostate boundaries, speckle noises and the short range of gray levels. In this paper, we propose the method that extracts a prostate boundary using features of its directions on prostate image. As a result of our experiments, it shows that the boundary never falls short of the existing methods or human expert's segmentation. And also, its searching speed is too fast because the method searches a smaller area that other methods.
참고문헌 (10)
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