CN110264450A - 一种基于多特征的肺部ct图像关联规则方法 - Google Patents

一种基于多特征的肺部ct图像关联规则方法 Download PDF

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CN110264450A
CN110264450A CN201910522643.5A CN201910522643A CN110264450A CN 110264450 A CN110264450 A CN 110264450A CN 201910522643 A CN201910522643 A CN 201910522643A CN 110264450 A CN110264450 A CN 110264450A
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华树成
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Abstract

本发明涉及一种基于多特征的肺部CT图像关联规则方法，采用特征提取的方法标注肺门气管区域内的特定点，之后利用动态区域增长算法结合阈值筛选，通过算法生成气管和支气管，标定后，将其剔除；特征提取也可以用来提取各种结节的标准特征参数，用于判断分割后的图形中是正常肺组织还是结节。也可以将提取的参数用于后面的支持向量机判断结节分类。大大减轻了医生的工作量，能够高效率无遗漏地识别可能恶化的肿瘤结节，并且形成直观的三维模型，为肿瘤的早期诊断和提供正确的治疗方案提供了直观可靠的科学依据。

Description

一种基于多特征的肺部CT图像关联规则方法
技术领域
本发明涉及肺癌诊断技术领域，具体是一种基于多特征的肺部CT图像关联规则方法。
背景技术
肺癌是对人类健康和生命威胁最大的恶性肿瘤之一。在我国，肺癌已成为恶性肿瘤首位死亡原因。如果肺癌能被早期诊断及治疗，患者5年生存率将从14％上升到49％。而作为肺癌早期诊断重要指标的肺部小结节检测就成为整个诊断流程中非常重要的一个环节。目前肺部小结节的CT鉴别诊断是放射科医生日常医疗工作中常面临的难题，现有肺部肿瘤影像学诊断是依据CT成像原理拍摄的图像，根据病灶图像形态表现和解剖学关系及医生以往的诊断经验而推断的结论。而现用于胸部诊断的CT肺部扫描多为二维黑白图像，对于一些特殊部位，医生很难通过观看图像来准确判断是否存在小结节，因此容易造成漏诊误诊。
发明内容
本发明所要解决的技术问题是提供一种基于多特征的肺部CT图像关联规则方法，以解决现有技术中存在的缺陷。
本发明解决上述技术问题的技术方案如下：
一种基于多特征的肺部CT图像关联规则方法，包括如下步骤：
第一步：获取并预处理肺部的CT图像：原始图像在冠状面，矢状面，轴向面三个维度的下采样；在下采样后的图像上应用均值0，标准差1.5的高斯模糊；背景去除方法采用自适应阈值法找骨骼区域，并且基于骨骼区域利用凸包技术找到胸腔区域；通过三个轴向均匀腐蚀的方法将胸腔区域按照肺部区域缩减，能有效去除胸廓；
第二步：对获取的图像进行图像分割及图像识别:将所述CT图像分别输入至预先建立的第一识别模型和第二识别模型中，输出对应的第一识别结果和第二识别结果；其中，所述第一识别模型通过神经网络训练而成，用于识别尺寸大于第一阈值的病灶区域；所述第二识别模型通过神经网络训练而成，用于识别尺寸小于第二阈值的病灶区域；
第三步：对识别的结果进行深度学习并分类：采用特征提取的方法标注特定点，之后利用动态区域增长算法结合阈值筛选，通过算法生成气管和支气管；将经过深度学习得出的结节的三维特征输出用于构建直观的结节三维模型，并进行3D输出；
第四步：通过数据特征比较判断结节类型是采用支持向量机处理，对结节的要素进行分类提取，从而判断结节的类型，然后对判断结果进行特征特征提取，并将特征提取结果用于第二步中的图像识别；
第五步：输出结果并结合诊断过程获得的其他信息进行联合分析判断，确认结果。
本发明的有益效果是：大大减轻了医生的工作量，能够高效率无遗漏地识别可能恶化的肿瘤结节，并且形成直观的三维模型，为肿瘤的早期诊断和提供正确的治疗方案提供了直观可靠的科学依据。
附图说明
图1为本发明结构示意图；
具体实施方式
以下结合附图对本发明的原理和特征进行描述，所举实例只用于解释本发明，并非用于限定本发明的范围。
如图1所示，一种基于多特征的肺部CT图像关联规则方法，包括如下步骤：
第一步：获取并预处理肺部的CT图像：原始图像在冠状面，矢状面，轴向面三个维度的下采样；在下采样后的图像上应用均值0，标准差1.5的高斯模糊；背景去除方法采用自适应阈值法找骨骼区域，并且基于骨骼区域利用凸包技术找到胸腔区域；通过三个轴向均匀腐蚀的方法将胸腔区域按照肺部区域缩减，能有效去除胸廓；
第二步：对获取的图像进行图像分割及图像识别:将所述CT图像分别输入至预先建立的第一识别模型和第二识别模型中，输出对应的第一识别结果和第二识别结果；其中，所述第一识别模型通过神经网络训练而成，用于识别尺寸大于第一阈值的病灶区域；所述第二识别模型通过神经网络训练而成，用于识别尺寸小于第二阈值的病灶区域；
第三步：对识别的结果进行深度学习并分类：采用特征提取的方法标注特定点，之后利用动态区域增长算法结合阈值筛选，通过算法生成气管和支气管；将经过深度学习得出的结节的三维特征输出用于构建直观的结节三维模型，并进行3D输出；
第四步：通过数据特征比较判断结节类型是采用支持向量机处理，对结节的要素进行分类提取，从而判断结节的类型，然后对判断结果进行特征特征提取，并将特征提取结果用于第二步中的图像识别；
第五步：输出结果并结合诊断过程获得的其他信息进行联合分析判断，确认结果。
以上所述仅为本发明的较佳实施例，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (1)

1.一种基于多特征的肺部CT图像关联规则方法，其特征在于：包括如下步骤：
S1：获取并预处理肺部的CT图像：原始图像在冠状面，矢状面，轴向面三个维度的下采样；在下采样后的图像上应用均值0，标准差1.5的高斯模糊；背景去除方法采用自适应阈值法找骨骼区域，并且基于骨骼区域利用凸包技术找到胸腔区域；通过三个轴向均匀腐蚀的方法将胸腔区域按照肺部区域缩减，能有效去除胸廓；
S2：对获取的图像进行图像分割及图像识别:将所述CT图像分别输入至预先建立的第一识别模型和第二识别模型中，输出对应的第一识别结果和第二识别结果；其中，所述第一识别模型通过神经网络训练而成，用于识别尺寸大于第一阈值的病灶区域；所述第二识别模型通过神经网络训练而成，用于识别尺寸小于第二阈值的病灶区域；
S3：对识别的结果进行深度学习并分类：采用特征提取的方法标注特定点，之后利用动态区域增长算法结合阈值筛选，通过算法生成气管和支气管；将经过深度学习得出的结节的三维特征输出用于构建直观的结节三维模型，并进行3D输出；
S4：通过数据特征比较判断结节类型是采用支持向量机处理，对结节的要素进行分类提取，从而判断结节的类型，然后对判断结果进行特征特征提取，并将特征提取结果用于第二步中的图像识别；
S5：输出结果并结合诊断过程获得的其他信息进行联合分析判断，确认结果。
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WO2008035286A2 (en) * 2006-09-22 2008-03-27 Koninklijke Philips Electronics N.V. Advanced computer-aided diagnosis of lung nodules
CN108399354A (zh) * 2017-02-08 2018-08-14 上海点医计算机科技有限公司 计算机视觉识别肿瘤的方法和装置
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111477303A (zh) * 2020-04-15 2020-07-31 吉林大学第一医院 基于数字减影技术的对位3d实时显示系统

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