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Automated analysis of a sequence of ovarian ultrasound images. Part I: segmentation of single SD images

机译:自动分析一系列卵巢超声图像。第一部分:单个SD图像的分割

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An improved algorithm is presented for ovarian follicle detection in ultrasound images. This full automated recognition algorithm is composed of tree successive steps. First, initial homogenous regions are determined. Then, these initial regions are grown. The growing is controlled by average grey-level and by a newly introduced weighted image gradient. In the last stage, those regions are extracted that probably correspond to the follicles. The algorithm has been tested on 50 ovarian ultrasound images. The recognition rate of follicles using this procedure was around 78/100. A possible extension of the algorithm deals with the entire information in the ultrasound image sequence, which is covered in Part II of this paper.
机译:提出了一种改进的算法,用于超声图像中的卵泡检测。这种全自动识别算法由树的连续步骤组成。首先,确定初始同质区域。然后,生长这些初始区域。增长由平均灰度级和新引入的加权图像梯度控制。在最后阶段,提取可能与卵泡相对应的那些区域。该算法已在50张卵巢超声图像上进行了测试。使用此程序的卵泡识别率约为78/100。该算法的可能扩展是处理超声图像序列中的全部信息,这将在本文的第二部分中介绍。

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