CN112634280A - 基于能量泛函的mri图像脑肿瘤分割方法 - Google Patents
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Abstract
本发明公开了一种基于能量泛函的MRI图像脑肿瘤分割方法,首先对脑部MRI图像进行预处理,然后提取病灶区域的局部强度特征、形状特征和面积特征,并将所提取特征融入能量泛函中,从而对脑部MRI图像进行分割。实验表明,本发明可有效提高脑肿瘤分割准确率。
Description
技术领域
本发明涉及数字图像分割领域,尤其是一种可有效提高分割准确率的基于能量泛函的MRI图像脑肿瘤分割方法。
背景技术
目前,磁共振成像(MRI)和计算机断层扫描(CT)成为肿瘤影像检测的主要方式。MRI图像因具有较高的分辨率和较多的参数,能够准确地描述脑部解剖结构,有效地诊断脑肿瘤,成为临床上较为常用的脑肿瘤影像检测方法。通过对MRI图像进行分割,可以得到脑肿瘤的形状、大小、位置和面积等特征,供医生对脑肿瘤区域的准确诊断和处理。近些年来,一种将图像特征融合于能量泛函的图像分割方法被广泛应用于图像分割领域。LiChunming等人提出了局部二值拟合算法,利用图像的局部信息特征进行分割;之后一些学者在此基础上进行了改进,融合了图像的局部信息特征和全局信息特征,提高了图像分割的准确度,但是因对肿瘤图像特征信息分析的不够准确,对图像边缘噪声处理的能力较差,分割的准确率较低。
发明内容
本发明是为了解决现有技术所存在的上述技术问题,提供一种可有效提高分割准确率的基于能量泛函的MRI图像脑肿瘤分割方法。
本发明的技术解决方案是:一种基于能量泛函的MRI图像脑肿瘤分割方法,依次按照如下步骤进行:
步骤2:计算局部区域W内的局部强度特征、形状特征和面积特征:
步骤2.1按照公式(1)计算局部强度特征FJ,
FJ=∫Ω(I(x,y)-J(W,c))2dxdy (1)
式中,J(W,c)代表平均强度;
步骤2.2按照公式(2)计算形状特征ES:
步骤2.3按照公式(3)计算面积特征EA:
式中,I是I(x,y)的简化形式;δ为Dirac函数,为H的积分形式;z1为水平集函数大于0的区域内的像素值,即病灶内部区域像素值,z2为水平集函数小于0的区域内的像素值,即病灶外部区域像素值;J1为病灶区域内的平均强度,J2病灶区域外的平均强度;
步骤5:检查演化曲线是否稳定收敛,若稳定收敛,则停止迭代;否则,转入步骤2。
本发明首先对脑部MRI图像进行预处理,然后提取病灶区域的局部强度特征、形状特征和面积特征,并将所提取特征融入能量泛函中,从而对脑部MRI图像进行分割。实验表明,本发明可有效提高脑肿瘤分割准确率。
附图说明
图1为本发明实施例方法步骤及结果示意图。
图2为本发明实施例进行MRI图像分割结果与迭代时间示意图。
图3为本发明实施例与现有技术在MRI图像上分割结果对比示意图。
具体实施方式
本发明一种基于能量泛函的MRI图像脑肿瘤分割方法,依次按照如下步骤进行:
步骤2:计算局部区域W内的局部强度特征、形状特征和面积特征:
步骤2.1按照公式(1)计算局部强度特征FJ,
FJ=∫Ω(I(x,y)-J(W,c))2dxdy (1)
式中,J(W,c)代表平均强度;
步骤2.2按照公式(2)计算形状特征ES:
步骤2.3按照公式(3)计算面积特征EA:
在实际应用中,根据脑肿瘤的区域特征确定脑部病变区域是良性或者恶性,需要将数据集乘以0.264(1像素=0.264mm),以接近实际病变面积;
式中,I是I(x,y)的简化形式;δ为Dirac函数,为H的积分形式;z1为水平集函数大于0的区域内的像素值,即病灶内部区域像素值,z2为水平集函数小于0的区域内的像素值,即病灶外部区域像素值;J1为病灶区域内的平均强度,J2病灶区域外的平均强度;
步骤5:检查演化曲线是否稳定收敛,若稳定收敛,则停止迭代;否则,转入步骤2。
本发明实施例对MRI图像脑肿瘤分割的方法及结果如图1所示。
本发明实施例进行MRI图像分割结果与迭代时间示意图如图2所示,结果表明,不同的MRI图像经过多次迭代均可以在较短的时间内准确得出分割结果。
本发明与现有技术(局部强度特征方法、纹理形状特征方法)分别以三张MRI图像为原图进行分割,结果如图3所示。结果表明,本发明提出的基于多种特征的分割方法能够得到更好的分割结果。
本发明与与现有技术(局部强度特征方法、纹理形状特征方法)在MRI图像上分割结果的定性分析如表1所示。
表1
结果表明,本发明所提出的方法在多个分割评价标准上,相较于单一特征进行分割的结果评价上具有一定的优势。
Claims (1)
1.一种基于能量泛函的MRI图像脑肿瘤分割方法,其特征在于依次按照如下步骤进行:
步骤2:计算局部区域W内的局部强度特征、形状特征和面积特征:
步骤2.1按照公式(1)计算局部强度特征FJ,
FJ=∫Ω(I(x,y)-J(W,c))2dxdy (1)
式中,J(W,c)代表平均强度;
步骤2.2按照公式(2)计算形状特征ES:
步骤2.3按照公式(3)计算面积特征EA:
式中,I是I(x,y)的简化形式;δ为Dirac函数,为H的积分形式;z1为水平集函数大于0的区域内的像素值,即病灶内部区域像素值,z2为水平集函数小于0的区域内的像素值,即病灶外部区域像素值;J1为病灶区域内的平均强度,J2病灶区域外的平均强度;
步骤5:检查演化曲线是否稳定收敛,若稳定收敛,则停止迭代;否则,转入步骤2。
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CN114565619A (zh) * | 2022-02-21 | 2022-05-31 | 辽宁师范大学 | 结合脑部解剖学特征的颅内血块能量泛函分割方法 |
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US20080260221A1 (en) * | 2007-04-20 | 2008-10-23 | Siemens Corporate Research, Inc. | System and Method for Lesion Segmentation in Whole Body Magnetic Resonance Images |
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CN114565619B (zh) * | 2022-02-21 | 2024-04-02 | 辽宁师范大学 | 结合脑部解剖学特征的颅内血块能量泛函分割方法 |
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