Atlas based AAM and SVM model for fully automatic MRI prostate segmentation.
Atlas based AAM and SVM model for fully automatic MRI prostate segmentation.
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DOI：
10.1109/embc.2014.6944225
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
McAuliffe MJ
中科院分区：
文献类型：
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作者：
Cheng R;Turkbey B;Gandler W;Agarwal HK;Shah VP;Bokinsky A;McCreedy E;Wang S;Sankineni S;Bernardo M;Pohida T;Choyke P;McAuliffe MJ
MR图像中的自动前列腺分割是一项具有挑战性的任务，这是由于患者间前列腺形状和纹理的变化，以及缺乏清晰的前列腺边界。我们提出了一个监督学习框架，结合基于图谱的AAM和SVM模型，以实现前列腺边界的相对较高的分割结果。在40个MR图像数据集上进行交叉验证，评估分割的性能，平均分割精度接近90%。
Automatic prostate segmentation in MR images is a challenging task due to inter-patient prostate shape and texture variability, and the lack of a clear prostate boundary. We propose a supervised learning framework that combines the atlas based AAM and SVM model to achieve a relatively high segmentation result of the prostate boundary. The performance of the segmentation is evaluated with cross validation on 40 MR image datasets, yielding an average segmentation accuracy near 90%.