Pachai C, Zhu Y M, Grimaud J, Hermier M, Dromigny-Badin A, Boudraa A, Gimenez G, Confavreux C, Froment J C
CREATIS, CNRS Research Unit UMR 5515, INSA 502, Villeurbanne, France.
Comput Med Imaging Graph. 1998 Sep-Oct;22(5):399-408. doi: 10.1016/s0895-6111(98)00049-4.
Quantitative assessment of Magnetic Resonance Imaging (MRI) lesion load of patients with multiple sclerosis (MS) is the most objective approach for a better understanding of the history of the pathology, either natural or modified by therapies. To achieve an accurate and reproducible quantification of MS lesions in conventional brain MRI, an automatic segmentation algorithm based on a multiresolution approach using pyramidal data structures is proposed. The systematic pyramidal decomposition in the frequency domain provides a robust and flexible low level tool for MR image analysis. Context-dependent rules regarding MRI findings in MS are used as high level considerations for automatic lesion detection.
对多发性硬化症(MS)患者的磁共振成像(MRI)病变负荷进行定量评估,是更好地了解病理过程(无论是自然病程还是经治疗改变的病程)的最客观方法。为了在传统脑部MRI中实现对MS病变的准确且可重复的量化,本文提出了一种基于使用金字塔数据结构的多分辨率方法的自动分割算法。频域中的系统金字塔分解为MR图像分析提供了一个强大且灵活的底层工具。关于MS中MRI表现的上下文相关规则被用作自动病变检测的高层考量因素。