Kalavathi P, Prasath V B Surya
Department of Computer Science and Applications, Gandhigram Rural Institute - Deemed University, Gandhigram, Tamil Nadu, 624302, India.
Computational Imaging and VisAnalysis (CIVA) Lab, Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA.
J Digit Imaging. 2016 Jun;29(3):365-79. doi: 10.1007/s10278-015-9847-8.
The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.
与功能图像(即正电子发射断层扫描(PET)、单光子发射计算机断层扫描(SPECT)和功能磁共振成像(fMRI))相比,高分辨率磁共振(MR)脑图像包含一些非脑组织,如皮肤、脂肪、肌肉、颈部和眼球,而功能图像通常包含相对较少的非脑组织。这些非脑组织的存在被认为是自动脑图像分割和分析技术的主要障碍。因此,对MR脑图像进行定量形态学研究通常需要进行预处理,以将脑与颅外或非脑组织分离,这通常被称为去颅骨。本文介绍了现有的去颅骨方法,并对近期有关现有去颅骨方法的文献进行了探索性综述。