DEVELOPMENT OF AUTOMATED SEGMENTATION TOOL FOR MOUSE BRAIN

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 MR显微镜-与研究人员共享MR小鼠大脑数据 开发将小鼠大脑自动分割成神经解剖结构的方法。具体地说，我们计划探索支持向量机(SVM)和马尔可夫随机场(MRF)在小鼠大脑分割中的适用性。支持向量机在脑PET图像分类、光学字符识别、目标检测、人脸识别、文本分类等领域有着广泛的应用。据我们所知，将支持向量机应用于小鼠脑结构分类的研究较少。最近的文献表明，Ali等人(2005)探索了马尔可夫随机场(MRF)在小鼠脑分割中的应用。考虑到研究对象的样本量较小，支持向量机往往比马尔可夫随机场等技术具有更好的结果。因此，我们想先进行比较研究。我们将比较支持向量机和马尔可夫随机场的分割结果。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. MR microscopy - shared MR mouse brain data with the investigator Develop methodology for automated segmentation of the mouse brain into neuroanatomical structures. In specifically, we are planning to explore the applicability of Support Vector Machine (SVM) and Markov Random Field (MRF) for mouse brain segmentation. SVMs have been applied to numerous fields including classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. To our knowledge, applying SVM to classify mouse brain structures has been less touched. Recently literature indicates Ali et al (2005) explored the use of Markov Random Field (MRF) for mouse brain segmentation. Given smaller sample size of subjects, SVM tends to have superior results than techniques such as MRF. Therefore, we would like to first conduct comparison study. We will compare the segmentation results of SVM with that of MRF.