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Automatic initialization of an active shape model of the prostate.

机译：自动初始化前列腺的活动形状模型。

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In this work is reported a new method for automatic segmentation of the boundary of the prostate, in transurethral ultrasound images. The scheme is based on a robust automatic initialization of an active shape model (ASM) of the prostate, which is subsequently fitted to the boundary of the gland. The initialization of the ASM is based on pixel classification to estimate the prostate region in an ultrasound image, followed by automatic adjustment - using a multipopulation genetic algorithm (MPGA) - of the initial pose of the ASM to the binary image produced by the classifier. The initial pose is next adjusted to the gray level ultrasound image, using the MPGA. After automatic initialization, the ASM is adjusted to the gray level ultrasound image to produce the final prostate contour. The method provides fast and robust segmentation of the prostate boundary. Validation results on 22 ultrasound images are reported with 1.74 mm of mean boundary error and an estimated processing time of 66 per image. Our automatic initialization method can be applied with the ASMs of different organs in various imaging modalities.
机译：在这项工作中，报道了一种经尿道超声图像自动分割前列腺边界的新方法。该方案基于对前列腺的主动形状​​模型（ASM）的强大自动初始化，随后将其拟合到腺体的边界。 ASM的初始化基于像素分类，以估计超声图像中的前列腺区域，然后将ASM的初始姿态自动调整（使用多种群遗传算法（MPGA））到分类器生成的二进制图像。接下来，使用MPGA将初始姿势调整为灰度超声图像。自动初始化后，将ASM调整为灰度超声图像以产生最终的前列腺轮廓。该方法提供了前列腺边界的快速和鲁棒的分割。报告了22幅超声图像的验证结果，平均边界误差为1.74毫米，每幅图像的估计处理时间为66。我们的自动初始化方法可以在各种成像方式中与不同器官的ASM一起使用。

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