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Curve Evolution Using Integration of Image Region and Edge Information To Detect Cyst

机译:利用图像区域和边缘信息的融合检测囊肿的曲线演化

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Image segmentation is very important to complete many image processing and computer vision tasks. It can be defined as a partitioning of the image into homogenous regions and semantic objects. One of the popular approaches to image segmentation is curve evolution and active contour models. The main aim of the paper is to segment the medical images to detect ultrasound cyst in order to aid telemedicine and to determine the edge information or regional properties or a combination of them. Curve evolution methods usually result in closed contours as opposed to disconnected edges resulting from filtering methods. Edge based active contours try to fit an initial closed contour to an edge function generated from the original image.
机译:图像分割对于完成许多图像处理和计算机视觉任务非常重要。它可以定义为将图像划分为同质区域和语义对象。流行的图像分割方法之一是曲线演化和主动轮廓模型。本文的主要目的是对医学图像进行分割以检测超声囊肿,以辅助远程医疗并确定边缘信息或区域特性或它们的组合。曲线演化方法通常导致闭合轮廓,而不是由滤波方法导致的不连续边缘。基于边缘的活动轮廓试图将初始闭合轮廓拟合到从原始图像生成的边缘函数。

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