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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >A Machine learning approach for interactive lesion segmentation
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A Machine learning approach for interactive lesion segmentation

机译：一种用于交互式病变分割的机器学习方法

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摘要

In this paper, we propose a novel machine learning approach for interactive lesion segmentation on CT and MRI images. Our approach consists of training process and segmenting process. In training process, we train AdaBoosted histogram classifiers to classify true boundary positions and false ones on the 1-D intensity profiles of lesion regions. In segmenting process, given a marker indicating a rough location of a lesion, the proposed solution segments its region automatically by using the trained AdaBoosted histogram classifiers. If there are imperfects in the segmented result, based on one correct location designated by the user, the solution does the segmentation again and gives a new satisfied result. There are two novelties in our approach. The first is that we use AdaBoost in the training process to learn diverse intensity distributions of lesion regions, and utilize the trained classifiers successfully in segmenting process. The second is that we present a reliable and user-friendly way in segmenting process to rectify the segmented result interactively. Dynamic programming is used to find a new optimal path. Experimental results show our approach can segment lesion regions successfully, despite the diverse intensity distributions of the lesion regions, marker location variability and lesion region shape variability. Our framework is also generic and can be applied for blob-like target segmentation with diverse intensity distributions in other applications.
机译：在本文中，我们提出了一种新颖的机器学习方法，用于在CT和MRI图像上进行交互式病变分割。我们的方法包括培训过程和细分过程。在训练过程中，我们训练AdaBoosted直方图分类器来对病变区域的一维强度分布图上的真实边界位置和虚假边界位置进行分类。在分割过程中，给定标记指示病变的大致位置，建议的解决方案通过使用训练有素的AdaBoosted直方图分类器自动分割其区域。如果分割结果中有不完善之处，则根据用户指定的一个正确位置，解决方案将再次进行分割并给出新的满意结果。我们的方法有两个新颖之处。首先，我们在训练过程中使用AdaBoost来学习病变区域的各种强度分布，并在分割过程中成功利用训练有素的分类器。第二个是我们在分割过程中提出了一种可靠且用户友好的方式来交互式地纠正分割结果。动态编程用于查找新的最佳路径。实验结果表明，尽管病变区域的强度分布，标记位置可变性和病变区域形状可变性各不相同，我们的方法仍可以成功地分割病变区域。我们的框架也是通用的，可以在其他应用程序中用于具有不同强度分布的类斑点目标分割。

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