一种全自动的脑部MR图像分割算法
合集下载
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
RESEARCH WORK
论wenku.baidu.com著
一种全自动的脑部MR图像分割算法
缪正飞，陈广浩，高伟
南京医科大学附属南京医院（南京市第一医院） 放射科，江苏 南京 210006 [摘 要] 目的 噪声和灰度不均使得脑部MR图像的全自动分割更具挑战性，本文提出一种改进的模糊C均值聚类算法，并将其 应用于脑部MR图像分割。方法 首先，采用同质滤波器和对比度扩展作为图像预处理，去除图像的奇异区域；然后，采用 直方图峰值点检测算法求得阈值点，避免初始聚类中心选取的盲目性；接着，采用模糊C均值聚类算法进行图像分割；最 后，采用基于模糊关联隶属度算法进行图像后处理，达到平滑模糊边界和去除噪声的效果，得到最终分割图像。结果 选用 噪声程度0~9%和灰度不均匀度0和40%的脑部MR图像进行仿真实验。视觉分析表明基于本文算法的分割图像边缘清晰，图 像质量优于没有预处理或后处理所得；定量评估结果显示基于本文分割算法获得的敏感性、特异性和相似性均高于改进的 模糊C均值算法和现存的FSL图像分割软件。结论 本文提出的算法收敛更快，能实现全自动分割脑部MR图像，在噪声和灰 度不均的情况下均表现出强健性、优越性和普适性，是一种可行的图像分割算法。 [关键词] 脑部MR图像；全自动分割；同质滤波；峰值检测；模糊C均值
引言
脑组织形态学改变的定量已成为一个重要的研究领域， 多种脑疾病伴随着脑组织形态学改变 [1-3]， 比如脱髓鞘疾病、 横贯性脊髓炎、精神分裂症等。脑部肿瘤区域大小的精确 测量对治疗剂量的确定至关重要，这就需要借助图像分割 技术将脑部 MR 图像分割为灰质、白质和脑脊液，且识别 出肿瘤和病变区域。 传统的手工分割容易对微小病变的鉴别造成误差，其 次对 MR 影像大数据的分析耗时费力。为此，近些年产生
A Novel Automatic Segmentation Approach for Brain MR Image
MIAO Zhengfei, CHEN Guanghao, GAO Wei Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing Jiangsu 210006, China Abstract: Objective Automated brain magnetic resonance (MR) image segmentation is a complex problem especially if accompanied by intensity inhomogeneity and noise. This paper proposed a modified fuzzy c-mean (MFCM) method which is used for the automatic segmentation of brain MR imaging. Methods The algorithm begined with a preprocessing step where we implemented automatic bias removal and contrast enhancement. This was followed by automated retrieval of mean intensity positions of various tissues detected. The corrected image was then passed on to an MFCM. The segmented result was further passed through neighborhood-based membership ambiguity correction which smooths the ambiguous boundaries and also removes pixel level noise between continuous regions of intensities. Results Brain Web normal brain simulated database with noise ranging from 0–9% and the inhomogeneity was 0 and 40% respectively. Qualitative evaluation results showed that the proposed method could provide clearer boundaries than that without pre- and post-processing. Quantitative evaluation results indicated that the improved active contour algorithm generated a higher degree of sensitivity, specificity and similarity than traditional FCM and FSL library tool-based software. Conclusion The proposed algorithm is of fully automatic segmentation, with faster computation and faster convergence of the objective function, which makes it be a feasible method for automatic brain MR segmentation. Key words: brain magnetic resonance image; automatic segmentation; homomorphic filtering technique; peaks retrieval; fuzzy c-mean [中图分类号] TP391 [文献标识码] A doi：10.3969/j.issn.1674-1633.2017.11.015 [文章编号] 1674-1633(2017)11-0061-05
论wenku.baidu.com著
一种全自动的脑部MR图像分割算法
缪正飞，陈广浩，高伟
南京医科大学附属南京医院（南京市第一医院） 放射科，江苏 南京 210006 [摘 要] 目的 噪声和灰度不均使得脑部MR图像的全自动分割更具挑战性，本文提出一种改进的模糊C均值聚类算法，并将其 应用于脑部MR图像分割。方法 首先，采用同质滤波器和对比度扩展作为图像预处理，去除图像的奇异区域；然后，采用 直方图峰值点检测算法求得阈值点，避免初始聚类中心选取的盲目性；接着，采用模糊C均值聚类算法进行图像分割；最 后，采用基于模糊关联隶属度算法进行图像后处理，达到平滑模糊边界和去除噪声的效果，得到最终分割图像。结果 选用 噪声程度0~9%和灰度不均匀度0和40%的脑部MR图像进行仿真实验。视觉分析表明基于本文算法的分割图像边缘清晰，图 像质量优于没有预处理或后处理所得；定量评估结果显示基于本文分割算法获得的敏感性、特异性和相似性均高于改进的 模糊C均值算法和现存的FSL图像分割软件。结论 本文提出的算法收敛更快，能实现全自动分割脑部MR图像，在噪声和灰 度不均的情况下均表现出强健性、优越性和普适性，是一种可行的图像分割算法。 [关键词] 脑部MR图像；全自动分割；同质滤波；峰值检测；模糊C均值
引言
脑组织形态学改变的定量已成为一个重要的研究领域， 多种脑疾病伴随着脑组织形态学改变 [1-3]， 比如脱髓鞘疾病、 横贯性脊髓炎、精神分裂症等。脑部肿瘤区域大小的精确 测量对治疗剂量的确定至关重要，这就需要借助图像分割 技术将脑部 MR 图像分割为灰质、白质和脑脊液，且识别 出肿瘤和病变区域。 传统的手工分割容易对微小病变的鉴别造成误差，其 次对 MR 影像大数据的分析耗时费力。为此，近些年产生
A Novel Automatic Segmentation Approach for Brain MR Image
MIAO Zhengfei, CHEN Guanghao, GAO Wei Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing Jiangsu 210006, China Abstract: Objective Automated brain magnetic resonance (MR) image segmentation is a complex problem especially if accompanied by intensity inhomogeneity and noise. This paper proposed a modified fuzzy c-mean (MFCM) method which is used for the automatic segmentation of brain MR imaging. Methods The algorithm begined with a preprocessing step where we implemented automatic bias removal and contrast enhancement. This was followed by automated retrieval of mean intensity positions of various tissues detected. The corrected image was then passed on to an MFCM. The segmented result was further passed through neighborhood-based membership ambiguity correction which smooths the ambiguous boundaries and also removes pixel level noise between continuous regions of intensities. Results Brain Web normal brain simulated database with noise ranging from 0–9% and the inhomogeneity was 0 and 40% respectively. Qualitative evaluation results showed that the proposed method could provide clearer boundaries than that without pre- and post-processing. Quantitative evaluation results indicated that the improved active contour algorithm generated a higher degree of sensitivity, specificity and similarity than traditional FCM and FSL library tool-based software. Conclusion The proposed algorithm is of fully automatic segmentation, with faster computation and faster convergence of the objective function, which makes it be a feasible method for automatic brain MR segmentation. Key words: brain magnetic resonance image; automatic segmentation; homomorphic filtering technique; peaks retrieval; fuzzy c-mean [中图分类号] TP391 [文献标识码] A doi：10.3969/j.issn.1674-1633.2017.11.015 [文章编号] 1674-1633(2017)11-0061-05