基于级联随机森林与活动轮廓的3D MR图像分割

Combining Concatenated Random Forests and Active Contour for the 3D MR Images Segmentation

针对医学磁共振(Magnetic resonance, MR)图像三维分割中随机森林(Random forest, RF)方法难以获得具有几何约束的结果以及活动轮廓模型(Active contour model, ACM)不能自动分割发生信号混叠的组织结构的问题,提出了一种整合了级联随机森林与活动轮廓模型的磁共振图像三维分割方法.该方法首先从多模态磁共振体数据中提取图像多尺度局部鲁棒统计信息,以此驱动级联随机森林对磁共振图像进行迭代的体素分类,从而获得对组织结构的初步分割结果,进一步将此结果作为初始轮廓与形状先验,整合进一个尺度可调的活动轮廓模型中,将独立的体素分类转化为轮廓曲线演化,最终得到具有几何约束的精确分割结果.在公开数据集上的实验结果表明,本文的自动化分割方法在分割精度和鲁棒性等方面,相比其他同类方法具有较大的性能提升.

Since it is difficult for the random forest(RF) method to achieve geometrically constrained result and the active contour model(ACM) can not segment tissue structures with overlapped signals automatically, when segmenting medical magnetic resonance(MR) images in three dimensions, a combined concatenated random forests and active contour model approach is proposed in this work for the 3D segmentation of medical magnetic resonance images. The multiscale local robust statistics image information is extracted from the multimodal magnetic resonance volumetric data, and then is used to drive the random forest to perform voxel classification iteratively. As a consequence, the initial segmentation result for the tissue structure is achieved. Furthermore, the initial result is integrated into a scale scalable active contour model as the initial contour and shape prior. In this way, the independent voxel classification is reformulated as contour evolution, and the final accurate and geometrically constrained segmentation result is achieved. Experimental results on publicly available datasets demonstrate that, compared to several related methods, the proposed automated segmentation method has considerable improvement in terms of segmentation accuracy and robustness.