DEVELOPMENT OF AUTOMATED SEGMENTATION TOOL FOR MOUSE BRAIN

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 MR显微镜检查-与研究者共享MR小鼠脑数据 开发将小鼠大脑自动分割成神经解剖结构的方法。具体来说，我们计划探索支持向量机（SVM）和马尔可夫随机场（MRF）的小鼠大脑分割的适用性。支持向量机已被广泛应用于脑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，但目前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）用于小鼠脑分割。由于受试者的样本量较小，SVM往往比MRF等技术具有上级结果。因此，我们想先进行比较研究。我们将比较SVM和MRF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.