Multifractal texture estimation for detection and segmentation of brain tumors.
Multifractal texture estimation for detection and segmentation of brain tumors.
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DOI：
10.1109/tbme.2013.2271383
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Iftekharuddin KM
中科院分区：
文献类型：
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作者：
Islam A;Reza SM;Iftekharuddin KM
提出了一种用于表征脑磁共振图像中肿瘤纹理的随机模型。该模型在磁共振图像中独立于患者的脑肿瘤纹理特征提取和肿瘤分割中得到了验证。由于脑肿瘤在MRI中的表现复杂，采用多分数布朗运动(MBM)的多分辨率-分维模型来描述脑肿瘤纹理。给出了MBM模型的详细数学推导以及相应的提取空间变化多重分形特征的新算法。接下来提出了一种基于多重分形特征的脑肿瘤分割方法。为了评价分割效果，将所提出的多重分形特征与Gabor类多尺度纹理特征的分割效果进行了比较。此外，通过对著名的AdaBoost算法进行扩展，提出了一种新的独立于患者的肿瘤分割方案。AdaBoost算法的改进包括基于组件分类器对困难样本的分类能力和对此类分类的置信度来为其分配权重。对14名患者的300多个磁共振成像的实验结果表明，该技术在脑部磁共振成像中自动分割肿瘤方面是有效的。最后，与其他最先进的脑肿瘤分割工作相比，使用公开可用的低级别胶质瘤BRATS2012数据集，我们的分割结果更加一致，并且在提供基本事实的情况下，平均而言，我们的分割结果优于这些方法。
A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available.