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首页> 外文期刊>Journal of Computer and Communications >Paraspinal Muscle Segmentation in CT Images Using GSM-Based Fuzzy C-Means Clustering
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Paraspinal Muscle Segmentation in CT Images Using GSM-Based Fuzzy C-Means Clustering

机译:基于基于GSM的模糊C均值聚类的CT图像中椎旁肌肉分割

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摘要

Minimally Invasive Spine surgery (MISS) was developed to treat disorders of the spine with less disruption to the muscles. Surgeons use CT images to monitor the volume of muscles after operation in order to evaluate the progress of patient recovery. The first step in the task is to segment the muscle regions from other tissues/organs in CT images. However, manual segmentation of muscle regions is not only inaccurate, but also time consuming. In this work, Gray Space Map (GSM) is used in fuzzy c-means clustering algorithm to segment muscle regions in CT images. GSM com- bines both spatial and intensity information of pixels. Experiments show that the proposed GSM- based fuzzy c-means clustering muscle CT image segmentation yields very good results.
机译:开发了微创脊柱外科手术(MISS)来治疗脊椎疾病并减少对肌肉的破坏。外科医生使用CT图像监测手术后的肌肉体积,以评估患者康复的进程。任务的第一步是从CT图像中将肌肉区域与其他组织/器官分开。但是,手动分割肌肉区域不仅不准确,而且很耗时。在这项工作中,灰色空间图(GSM)用于模糊c均值聚类算法,以分割CT图像中的肌肉区域。 GSM结合了像素的空间和强度信息。实验表明,所提出的基于GSM的模糊c均值聚类肌肉CT图像分割效果非常好。

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