【作者】 杜大莉；

【导师】 王超；

【作者基本信息】 天津大学 ， 检测技术与自动化装置， 2008， 硕士

【摘要】 生物电阻抗测量技术(Bio-Impedance Measuring Technology,BIMT)是利用生物组织与器官的电特性及其变化,提取与人体生理、病理相关信息的一种无损伤检测技术。该方法具有无创、廉价、安全、操作简单和信息丰富等特点,因此在医学界,尤其是在临床诊断中受到专家学者的重视,有着广泛的应用前景。本课题针对当前乳腺癌治疗手段中的保乳手术,将生物电阻抗测量技术应用到术中乳腺癌灶边缘界定中,实时评价手术切缘,帮助施术医师在手术中合理选择组织切除范围,从而在手术结束前清除残留病灶。这样可减轻患者心理和生理上的痛苦,具有重要的临床意义。本课题重点研究乳腺癌组织与正常组织(腺体和脂肪组织)电特性差异,从组织频阻特性曲线中寻找特征参数,对乳腺组织进行识别。通过对临床数据的研究分析,发现组织的频阻斜率信息可以用来对组织进行辨识。因此利用特性曲线在特殊频段内的斜率值和多个连续频段内的斜率值分别对三种组织区分。针对参数多,模型未知等特点,建立BP网络模型,研究神经网络集成方法,应用神经网络知识对组织进行辨识。通过研究,发现BP神经网络对于多个参数的输入有较好的分类能力,经过反复多次训练出来的神经网络具有良好的组织区分能力。集成后的神经网络对乳腺各种组织识别的敏感性和特异性更佳。更多还原

【Abstract】 Bio-Impedance Measuring Technology (BIMT) is a noninvasive measuring technology, the purpose of which is to extract physiological and pathological information by detecting the electrical properties of tissues and organs. It has the feature of low-cost, safety, easy-operating and wealth of information. So it is been paid special attention by the researchers in clinical medicine areas.Based on the bio-impedance measurement technology, this paper presents the intraoperative margin assessment of breast cancer for purpose of breast conserving surgery. It can help the surgeon judge the margin instantly and then choose the excising area reasonably. So that the remained focus could be cleaned in time. It can shorten the waiting time and lighten patients’pain in partial mastectomy.This paper focuses on researching the differences of electrical properties between breast cancer and normal breast tissues (mammary gland and adipose). The characteristic parameters searched from the frequency-resistance curve were studied to distinguish different tissues. Based on the analysis of clinical data, frequency-resistance slope information was found good enough to be used in the recognition of breast tissues. This paper utilized the slopes of special frequencies and multi- frequencies to identify three different tissues. Because of the multi-parameters, uncertain model and so on, the BP network was established and the integration of several neural networks was researched to recognize breast tissues.Results of the research indicated that BP network had good capability of classification while there were many input parameters. BP networks trained for many times have nice abilities to distinguish breast cancer from normal tissues. The integral neural network possessed high sensibility and specificity to the recognition of breast tissues.更多还原