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An Automatic Segmentation Algorithm of Boundary Layers from B-Mode Ultrasound Images

机译:B型超声图像边界层自动分割算法

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In image segmentation technique, partition an image into meaningful regions with respect to a particular application. In a digital image, pixels of a region have some common calculated properties like color, intensity, texture, depth, motion, etc. These properties are used to cluster pixels of a region in the given image. Segmented images are more meaningful, easy to analyze and are used to locate objects and boundaries. Segmentation of an image is the preliminary step to pattern recognition. Here, we propose an automatic segmentation method of the boundary layers for calculating the value of Intima-Media Thickness (IMT) from lumen-intima & media-adventitia layers without any user intervention from the B-mode ultrasound image of the common carotid artery. The main objective of our work is to explore the possibility and feasibility of a computational approach for the automatic segmentation of boundary layers.
机译:在图像分割技术中,针对特定应用程序将图像划分为有意义的区域。在数字图像中,区域的像素具有一些常见的计算属性,例如颜色,强度,纹理,深度,运动等。这些属性用于在给定图像中聚类区域的像素。分割后的图像更有意义,更易于分析,并用于定位对象和边界。图像分割是模式识别的初步步骤。在这里,我们提出了一种边界层的自动分割方法,用于从内膜内膜层和中膜外膜层计算内膜中膜厚度(IMT)的值,而无需用户从颈总动脉的B型超声图像中进行干预。我们工作的主要目的是探索边界层自动分割的一种计算方法的可能性和可行性。

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