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一种用于前列腺二维超声图像中恶性肿瘤定位的自动神经模糊方法。

An automated neural-fuzzy approach to malignant tumor localization in 2D ultrasonic images of the prostate.

机构信息

Department of Electrical and Computer Engineering, University of Waterloo, 619 Honeywood Place, Waterloo, ON, Canada.

出版信息

J Digit Imaging. 2011 Jun;24(3):411-23. doi: 10.1007/s10278-010-9301-x.

Abstract

In this paper, a new neural-fuzzy approach is proposed for automated region segmentation in transrectal ultrasound images of the prostate. The goal of region segmentation is to identify suspicious regions in the prostate in order to provide decision support for the diagnosis of prostate cancer. The new automated region segmentation system uses expert knowledge as well as both textural and spatial features in the image to accomplish the segmentation. The textural information is extracted by two recurrent random pulsed neural networks trained by two sets of data (a suspicious tissues' data set and a normal tissues' data set). Spatial information is captured by the atlas-based reference approach and is represented as fuzzy membership functions. The textural and spatial features are synthesized by a fuzzy inference system, which provides a binary classification of the region to be evaluated.

摘要

本文提出了一种新的神经模糊方法,用于经直肠前列腺超声图像的自动区域分割。区域分割的目标是识别前列腺中的可疑区域,以便为前列腺癌的诊断提供决策支持。新的自动区域分割系统利用专家知识以及图像中的纹理和空间特征来完成分割。纹理信息由两个通过两组数据(可疑组织数据集和正常组织数据集)进行训练的递归随机脉冲神经网络提取。空间信息由基于图谱的参考方法捕获,并表示为模糊隶属函数。纹理和空间特征由模糊推理系统综合,该系统对要评估的区域进行二进制分类。

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本文引用的文献

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