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Automatic Ischemic Stroke Lesion Segmentation Using Single MR Modality and Gravitational Histogram Optimization Based Brain Segmentation

机译:基于单一MR模态的自动缺血性卒中病变分割,基于重力直方图优化的大脑分割

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In this paper the automatic and customized brain segmentation followed by a stroke lesion detection technique is presented applying single modality Magnetic Resonance Images (MRIs). A novel intensity-based segmentation technique called gravitational histogram optimization is developed for this purpose. By applying histogram gravitational optimization algorithm the brain can be segmented into discriminative area including stroke lesion. The mathematical descriptions as well as the convergence criteria of the developed algorithm are presented in detail. The application of the proposed algorithm in the segmentation of single Diffusion-Weighted Images (DWI) modality of healthy and lesion MR image slices for different number of segments is presented and the results are discussed. The segmented areas are then employed in automatic lesion slice detection and lesion extraction technique. The stroke lesion is extracted from the recognized lesion slice with acceptable accuracy.
机译:在本文中,介绍了施加单个模态磁共振图像(MRIS)的自动和定制的脑分割后跟行程病变检测技术。为此目的开发了一种名为重力直方图优化的新型基于强度的分段技术。通过施加直方图重力优化算法,大脑可以分段成判别区域,包括行程病变。详细介绍了显影算法的数学描述以及发达算法的收敛标准。提出了在单个扩散加权图像(DWI)模块的分割中的应用算法的应用以及不同数量的段的图像切片的模态,并讨论了结果。然后采用分段区域以自动病变切片检测和病变提取技术。从识别的病变切片中提取行程病变,具有可接受的精度。

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