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首页> 外文期刊>Journal of visual communication & image representation >Integrating machine learning with region-based active contour models in medical image segmentation
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Integrating machine learning with region-based active contour models in medical image segmentation

机译：在医学图像分割中将机器学习与基于区域的活动轮廓模型集成

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摘要

Region-based active contour models are effective in segmenting images with poorly defined boundaries but often fail when applied to images containing intensity inhomogeneity. The traditional models utilize pixel intensity and are very sensitive to parameter tuning. On the other hand, machine learning algorithms are highly effective in handling inhomogeneities but often result in noise from misclassified pixels. In addition, there is no objective function. We propose a framework which integrates machine learning with a region-based active contour model. Classification probability scores from machine learning algorithm, which are regularized using a non-linear function, are used to replace the pixel intensity values during energy minimization. In our experiments, we integrate the k-nearest neighbours and the support vector machine with the Chan-Vese method and compare the results obtained with the traditional methods of Chan-Vese and Li et al. The proposed framework gives better accuracy and less sensitive to parameter tuning. (C) 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.
机译：基于区域的主动轮廓模型可以有效地分割边界定义不清晰的图像，但是当应用于包含强度不均匀性的图像时，通常会失败。传统模型利用像素强度，并且对参数调整非常敏感。另一方面，机器学习算法在处理不均匀性方面非常有效，但通常会导致像素分类错误。另外，没有目标函数。我们提出了一个框架，该框架将机器学习与基于区域的活动轮廓模型相集成。来自机器学习算法的分类概率评分（使用非线性函数进行正则化）用于在能量最小化期间​​替换像素强度值。在我们的实验中，我们将k最近邻和支持向量机与Chan-Vese方法进行了集成，并比较了与Chan-Vese和Li等人的传统方法获得的结果。所提出的框架提供了更好的准确性，并且对参数调整的敏感性降低。 （C）2016作者。由Elsevier Inc.发行。这是CC BY-NC-ND许可下的开放获取文章。

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