A cost-sensitive extension of AdaBoost with markov random field priors for automated segmentation of breast tumors in ultrasonic images
A cost-sensitive extension of AdaBoost with markov random field priors for automated segmentation of breast tumors in ultrasonic images
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DOI：
10.1007/s11548-010-0411-1
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发表时间：
2010-06
影响因子：
3
通讯作者：
A. Takemura;A. Shimizu;K. Hamamoto
中科院分区：
文献类型：
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作者：
A. Takemura;A. Shimizu;K. Hamamoto
目的基于马尔可夫随机场（MRF）先验知识，对AdaBoost进行代价敏感扩展，训练出一种能够避免不规则形状、孤立点和空洞的集成分割过程，从而降低错误率。方法在AdaBoost算法中引入代价函数，对MRF正则化中的相邻标签进行惩罚。扩展的AdaBoost算法通过依次选择错误率按代价加权最小的过程来生成一系列弱分割过程。该方法进行了测试，通过生成一个整体分割过程中的超声图像中的乳腺肿瘤。这是其次是一个活动轮廓细化提取的肿瘤boundary.ResultsThe分割性能进行了评估，通过十倍交叉验证测试，其中300个癌，50个纤维腺瘤，和50个囊肿。实验结果表明，该集成分割的错误率是三分之二的分割训练的AdaBoost没有MRF的错误率。通过将集成分割与测地线活动轮廓相结合，提取的肿瘤和手动分割的真实区域之间的平均Jaccard指数为93.41%，显着高于传统的分割process.ConclusionA成本敏感的扩展AdaBoost基于MRF先验提供了一种有效和准确的手段，用于分割乳腺超声图像中的肿瘤。
PurposeA cost-sensitive extension of AdaBoost based on Markov random field (MRF) priors was developed to train an ensemble segmentation process which can avoid irregular shape, isolated points and holes, leading to lower error rate. The method was applied to breast tumor segmentation in ultrasonic images.MethodsA cost function was introduced into the AdaBoost algorithm that penalizes dissimilar adjacent labels in MRF regularization. The extended AdaBoost algorithm generates a series of weak segmentation processes by sequentially selecting a process whose error rate weighted by the cost is minimum. The method was tested by generation of an ensemble segmentation process for breast tumors in ultrasonic images. This was followed by a active contour to refine the extracted tumor boundary.ResultsThe segmentation performance was evaluated by tenfold cross validation test, where 300 carcinomas, 50 fibroadenomas, and 50 cysts were used. The experimental results revealed that the error rate of the proposed ensemble segmentation was two-thirds the error rate of the segmentation trained by AdaBoost without MRF. By combining the ensemble segmentation with a geodesic active contour, the average Jaccard index between the extracted tumors and the manually segmented true regions was 93.41%, significantly higher than the conventional segmentation process.ConclusionA cost-sensitive extension of AdaBoost based on MRF priors provides an efficient and accurate means for the segmentation of tumors in breast ultrasound images.