学习使用机器学习惩罚逻辑模型进行分割

Yong Yue, H. Tagare
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引用次数: 6

摘要

经典的最大后验分割(MAP)使用生成模型对图像进行分割。然而,对于复杂的图像,创建易于处理的生成模型可能很困难。此外,生成模型要求在最大化过程中包含辅助参数,这使得最大化过程更加复杂。本文提出了一种替代MAP方法的方法:使用惩罚逻辑模型直接对分割后验进行建模。这种方法有两个优点:(1)它需要更少的辅助参数,(2)它提供了一种将强大的机器学习方法纳入分割的标准方法,以便可以从训练集中轻松学习复杂的图像现象。该技术用于分割心脏超声图像序列,这些图像序列具有大量的时空对比变化,难以建模。实验结果表明,该方法在对比度变化的情况下仍能准确地分割心内膜。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning to segment using machine-learned penalized logistic models
Classical maximum-a-posteriori (MAP) segmentation uses generative models for images. However, creating tractable generative models can be difficult for complex images. Moreover, generative models require auxiliary parameters to be included in the maximization, which makes the maximization more complicated. This paper proposes an alternative to the MAP approach: using a penalized logistic model to directly model the segmentation posterior. This approach has two advantages: (1) It requires fewer auxiliary parameters, and (2) it provides a standard way of incorporating powerful machine-learning methods into segmentation so that complex image phenomenon can be learned easily from a training set. The technique is used to segment cardiac ultrasound images sequences which have substantial spatio-temporal contrast variation that is cumbersome to model. Experimental results show that the method gives accurate segmentations of the endocardium in spite of the contrast variation.
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