CN104794708A - Atherosclerosis plaque composition dividing method based on multi-feature learning - Google Patents
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Abstract
一种基于多特征学习的动脉粥样硬化斑块成分分割方法,包括以下步骤:1)获取多序列动脉血管斑块图像;2)图像预处理;3)斑块特征提取及描述;4)分类器训练:分类器模型是支持向量机SVM;最后四种序列MRI动脉斑块图像共生成4个特征集,每一个特征集训练生成一个分类器,最终得到4个分类器,即lipid分类器,fibrous分类器,calcium分类器,hemorrhage分类器;5)训练器优化;6)对于斑块组织的某一个像素分别用四个分类器计算其隶属于不同组织的程度,根据得到的隶属度竞争排序,把隶属度最高的成分作为最终结果。本发明精确性良好、鲁棒性较高。
A method for segmenting atherosclerotic plaque components based on multi-feature learning, comprising the following steps: 1) acquiring multi-sequence arterial plaque images; 2) image preprocessing; 3) plaque feature extraction and description; 4) classification Trainer training: the classifier model is a support vector machine (SVM); the last four sequences of MRI arterial plaque images generate a total of 4 feature sets, each feature set is trained to generate a classifier, and finally 4 classifiers are obtained, namely the lipid classifier, Fibrous classifier, calcium classifier, hemorrhage classifier; 5) Trainer optimization; 6) For a certain pixel of plaque tissue, use four classifiers to calculate the degree of its belonging to different tissues, and sort according to the obtained degree of membership competition , and take the component with the highest degree of membership as the final result. The invention has good precision and high robustness.
Description
技术领域 technical field
本发明属于医学图像处理领域,尤其涉及到于计算机辅助和人工智能技术在医学图像分析方面的应用。 The invention belongs to the field of medical image processing, and in particular relates to the application of computer aided and artificial intelligence technology in medical image analysis.
背景技术 Background technique
当前,对于动脉粥样硬化斑块成分分割方法尚没有统一的框架和技术手段,其主要原因在于当前血管斑块的成像技术还不够成熟,这使得血管斑块的成像质量(例如斑块的图像的对比度、边缘的清晰度等)不高。另外当前血管图像一般都有灰度图像或者伪彩色图像,这使得计算机很难能够通过定量的数字量表征斑块信息。为了解决这些问题,国内外学者做了大量的研究工作,其中最具代表性的则是F.Liu等人提出的基于贝叶斯理论的斑块分割方法,该方法主要利用斑块的灰度值、斑块在血管中的位置以及形态信息作为特征,然后基于这些特征训练出一个高斯模型,利用贝叶斯后验概率公式计算出斑块隶属于某种成分的概率以完成成分的分割,但是该方法对图像的噪声很敏感,另外训练出一个能够刻画斑块特征的模型需要大量的训练样本。还有一些研究者利用模糊聚类的(Fuzzy-clustering)的方法分割斑块成分。该方法利用动脉斑块的灰度值作为特征然后利用模糊聚类的方式计算斑块隶属于不同成分的概率,这种方法缺点很明显,首先是该方法利用的斑块特征单一,不能够充分表达斑块的信息,其次该方法鲁棒性较低,模糊聚类的结果容易受到斑块成分的影响。国内研究者钱华明等提出了结合时域信息的区域生长算法,该算法利用图像之间的灰度差 异作为生长条件,并结合灰度信息作为生长条件,然后利用形态学的相关造作方法对分割结果做修正进而提取出不同成分的分割边界,但是该方法同样存在斑块特征表达不足和算法鲁棒性不高的缺陷。 At present, there is no unified framework and technical means for the segmentation of atherosclerotic plaque components. The main reason is that the current imaging technology of vascular plaque is not mature enough, which makes the imaging quality of vascular plaque (such as contrast, sharpness of edges, etc.) is not high. In addition, current vascular images generally have grayscale images or pseudo-color images, which makes it difficult for computers to represent plaque information through quantitative digital quantities. In order to solve these problems, scholars at home and abroad have done a lot of research work, the most representative of which is the plaque segmentation method based on Bayesian theory proposed by F. Liu et al. This method mainly uses the gray level of the plaque The value, the position of the plaque in the blood vessel, and the shape information are used as features, and then a Gaussian model is trained based on these features, and the Bayesian posterior probability formula is used to calculate the probability that the plaque belongs to a certain component to complete the component segmentation. However, this method is very sensitive to image noise, and training a model that can describe plaque characteristics requires a large number of training samples. Some researchers use fuzzy-clustering method to segment plaque components. This method uses the gray value of arterial plaque as a feature and then uses fuzzy clustering to calculate the probability of plaque belonging to different components. To express the information of plaques, secondly, the method is less robust, and the result of fuzzy clustering is easily affected by plaque components. Domestic researcher Qian Huaming proposed a region growing algorithm combined with time domain information. This algorithm uses the gray level difference between images as the growth condition, and combines the gray level information as the growth condition, and then uses the morphological correlation method to The segmentation results are corrected to extract the segmentation boundaries of different components, but this method also has the defects of insufficient expression of plaque features and low robustness of the algorithm.
发明内容 Contents of the invention
为了克服已有动脉粥样硬化斑块成分分割方法的精确性较差、鲁棒性不高的不足,本发明提供了一种精确性良好、鲁棒性较高的基于多特征学习的动脉粥样硬化斑块成分分割方法。 In order to overcome the disadvantages of poor accuracy and low robustness of existing atherosclerotic plaque component segmentation methods, the present invention provides a multi-feature learning-based atherosclerotic model with good accuracy and high robustness. Segmentation of sclerotic plaque components.
本发明解决其技术问题所采用的技术方案是: The technical solution adopted by the present invention to solve its technical problems is:
一种基于多特征学习的动脉粥样硬化斑块成分分割方法,所述分割方法包括以下步骤: A method for segmentation of atherosclerotic plaque components based on multi-feature learning, the segmentation method comprising the following steps:
1)获取多序列动脉血管斑块图像; 1) Acquiring multi-sequence arterial plaque images;
2)图像预处理 2) Image preprocessing
选取以动脉血管内壁为中心的ROI作为研究区域,对选取的ROI做灰度值的窗宽、窗位调整,然后对ROI做去噪处理; Select the ROI centered on the inner wall of the artery as the research area, adjust the window width and window level of the gray value of the selected ROI, and then denoise the ROI;
3)斑块特征提取及描述 3) Plaque feature extraction and description
从斑块图像的灰度值、高斯模糊特征、梯度值、斑块到血管内外壁的最近距离、图像的拉普拉斯特征以及斑块的纹理特征分别描述斑块的特征; The characteristics of the plaque are described from the gray value of the plaque image, the Gaussian blur feature, the gradient value, the shortest distance between the plaque and the inner and outer walls of the blood vessel, the Laplace feature of the image and the texture feature of the plaque;
4)分类器训练:分类器模型是支持向量机SVM,训练过程如下: 4) Classifier training: The classifier model is a support vector machine SVM, and the training process is as follows:
(4.1)训练样本的收集与标定 (4.1) Collection and calibration of training samples
训练样本中,T1序列图像作为参考图像,医生手动标定动脉粥样硬化斑块成分是在T1图像上操作,其他四种序列T1GD、T2、PD和STIR的斑块成分是按照T1标定的结果映射在其上; In the training sample, the T1 sequence image is used as the reference image, and the manual calibration of the atherosclerotic plaque components by the doctor is performed on the T1 image, and the plaque components of the other four sequences T1GD, T2, PD, and STIR are mapped according to the results of T1 calibration on it;
(4.2)样本预处理 (4.2) Sample preprocessing
首先对样本图像做灰度值拉伸操作,使其灰度值分布范围在[0,255]区间范围,然后是对图像去噪声处理,最后是把医生的对斑块成分的标定结果做二值化处理,即把医生标定的斑块的每一种成分储存在的二值矩阵中; First, the gray value stretching operation is performed on the sample image so that the gray value distribution range is in the range of [0,255], then the image is denoised, and finally the doctor's calibration result of the plaque components is binarized Processing, that is, storing each component of the plaque calibrated by the doctor in the binary matrix;
(4.3)训练样本进一步分类和特征量化处理 (4.3) Further classification and feature quantization processing of training samples
训练样本的进一步分类是把样本细分为lipid样本集、fibrous样本集、calcium样本集以及hemorrhage样本集; The further classification of training samples is to subdivide the samples into lipid sample set, fibrous sample set, calcium sample set and hemorrhage sample set;
特征量化处理是把已经提取到的斑块特征做归一化处理,所有特征经过归一化之后形成一个特征向量,该特征向量如式(10)所示。 The feature quantization process is to normalize the extracted plaque features, and all the features are normalized to form a feature vector, the feature vector As shown in formula (10).
式中,V表示斑块的灰度值,G表示斑块的高斯模糊值,GM表示灰度值高斯模糊后的灰度特征,L表示拉普拉斯特征,D表示斑块空间位置特征,T表示斑块的纹理特征。 In the formula, V represents the gray value of the plaque, G represents the Gaussian blur value of the plaque, GM represents the gray feature after the Gaussian blur of the gray value, L represents the Laplacian feature, and D represents the spatial position feature of the plaque, T represents the texture feature of the plaque.
(4.4)分类器训练,最后四种序列MRI动脉斑块图像共生成4个特征集,每一个特征集训练生成一个分类器,最终得到4个分类器,即lipid分类器,fibrous分类器,calcium分类器,hemorrhage分类器; (4.4) Classifier training, the last four sequences of MRI arterial plaque images generate 4 feature sets in total, each feature set is trained to generate a classifier, and finally 4 classifiers are obtained, namely lipid classifier, fibrous classifier, calcium classifier, hemorrhage classifier;
5)训练器优化 5) Trainer optimization
把样本集分为训练集和测试集两个部分,然后用训练集的样本训练分类器,用得到的分类器检验测试集的识别率,依次判断循环下去,直到得到的训练器分类正确率达到最大值为止,此时得到的分类器认为是最优分类器; Divide the sample set into two parts, the training set and the test set, and then use the samples of the training set to train the classifier, use the obtained classifier to test the recognition rate of the test set, and continue to judge and cycle until the obtained trainer classification accuracy reaches Up to the maximum value, the classifier obtained at this time is considered to be the optimal classifier;
6)对于斑块组织的某一个像素分别用四个分类器计算其隶属于不 同组织的程度,过程如下: 6) For a certain pixel of plaque tissue, four classifiers are used to calculate the degree of its belonging to different tissues, the process is as follows:
(6.1)首先把T1GD、T2、PD和STIR斑块组织图像在每个像素点出的特征提取出来; (6.1) First extract the features of each pixel of T1GD, T2, PD and STIR plaque tissue images;
(6.2)把(6.1)中提取的斑块特征分别放到步骤(5)中优化后的四个分类器中,四种分类器分别计算其隶属于其成分的程度; (6.2) Put the plaque features extracted in (6.1) into the four classifiers optimized in step (5) respectively, and the four classifiers calculate the degree to which they belong to their components respectively;
(6.3)根据得到的(6.2)中的隶属度竞争排序,把隶属度最高的成分作为最终结果。 (6.3) According to the competitive ranking of the membership degree obtained in (6.2), take the component with the highest membership degree as the final result.
进一步,所述分割方法还包括以下步骤: Further, the segmentation method also includes the following steps:
7)结果的形态学操作,过程如下: 7) Morphological operation of the result, the process is as follows:
(7.1)把成分分割的结果存储在4个二值图像中,二值图像中1代表组织成分,0代表背景,分别是lipid成分、fibrous成分、calcium成分,hemorrhage成分; (7.1) Store the result of component segmentation in four binary images, where 1 represents the tissue component and 0 represents the background in the binary image, which are the lipid component, fibrous component, calcium component, and hemorrhage component;
(7.2)然后对每一个二值图像做腐蚀运算,把孤立的分割块滤除; (7.2) Then perform corrosion operation on each binary image, and filter out isolated segmentation blocks;
(7.3)然后做形态学开运算,填补成分块中的空洞以及连通相近的孤立块; (7.3) Then do the morphological opening operation to fill the holes in the component blocks and connect similar isolated blocks;
(7.4)最后做图像闭运算,得到动脉粥样硬化斑块成分。 (7.4) Finally, image closing operation is performed to obtain atherosclerotic plaque components.
更进一步,所述步骤3)中, Further, in the step 3),
(a)灰度值为在图像经过去噪处理后的灰度值; (a) The gray value is the gray value after the image has been denoised;
(b)高斯模糊特征 (b) Gaussian blur feature
所述高斯模糊特征指的是斑块组织某一点经过高斯模糊之后的灰度值,提取过程是采用一个5*5的高斯核与斑块组织做卷积,如式(2)所示。 The Gaussian blur feature refers to the gray value of a certain point of the plaque tissue after Gaussian blurring, and the extraction process is to use a 5*5 Gaussian kernel to perform convolution with the plaque tissue, as shown in formula (2).
式中,σ表示高斯核的尺度,x表示图像的灰度值,I,G分别代表图像原始血管斑块图像和高斯模糊后的图像,“*”是卷积符号; In the formula, σ represents the scale of the Gaussian kernel, x represents the gray value of the image, I and G represent the original vascular plaque image and the image after Gaussian blur respectively, and “*” is the convolution symbol;
(c)梯度特征 (c) Gradient features
斑块组织的梯度特征GM: Gradient feature GM of plaque tissue:
(d)拉普拉斯特征L: (d) Laplace feature L:
(e)斑块空间位置特征(到内外壁之间的距离) (e) Spatial location characteristics of plaques (distance to inner and outer walls)
Dmin(I)=min||C(I(x))-Lumen(I)|| (5) D min (I)=min||C(I(x))-Lumen(I)|| (5)
Dmax(I)=max||C(I(x))-Lumen(I)|| (6) D max (I)=max||C(I(x))-Lumen(I)|| (6)
(f)纹理特征 (f) Texture features
提取了一阶统计量和灰度共生矩阵特征两类纹理特征,共七个一阶统计量,其中包括斑块内部灰度均值、标准差、差异系数、偏度、峰度、直方图熵和亮度熵;EtHis如式(7)和亮度熵EtBrt如式(8): Two types of texture features, the first-order statistics and the gray-level co-occurrence matrix features, are extracted, and there are seven first-order statistics in total, including the gray mean, standard deviation, coefficient of difference, skewness, kurtosis, histogram entropy and Luminance entropy; EtHis as formula (7) and brightness entropy EtBrt as formula (8):
其中,Pi(i=0,1,...,255)是灰阶i在图像中出现的概率,ωi是归一化后不为0的像素强度,N是原图像的像素总数。 Among them, P i (i=0, 1, . . . , 255) is the probability of grayscale i appearing in the image, ω i is the pixel intensity that is not 0 after normalization, and N is the total number of pixels in the original image.
本发明的有益效果主要表现在:利用多序列磁共振图像(multiple sequence MRI)的作为图像源,然后从多方面提取动脉粥样硬化斑块的特征,利用机器学习的方法训练分类器,最终完成斑块成分的分割;精确性良好、鲁棒性较高。 The beneficial effects of the present invention are mainly manifested in: using multiple sequence MRI images as an image source, then extracting the characteristics of atherosclerotic plaque from various aspects, using machine learning methods to train classifiers, and finally completing Segmentation of plaque components; good accuracy and high robustness.
附图说明 Description of drawings
图1是基于多特征学习的动脉粥样硬化斑块成分分割方法的流程图。 Figure 1 is a flowchart of a method for segmenting atherosclerotic plaque components based on multi-feature learning.
图2是ROI选取与去噪结果图,其中,(a)为ROI选取示意图,(b)为去噪结果图。 FIG. 2 is a diagram of ROI selection and denoising results, wherein (a) is a schematic diagram of ROI selection, and (b) is a diagram of denoising results.
图3是多序列磁共振图像的配准结果图,其中,(a)为配准前T1(左边图)和PD(右边图)的空间差异图,(b)为配准结果,T1(左边图)和PD(右边图)示意图。 Fig. 3 is the registration result map of multi-sequence magnetic resonance images, in which (a) is the spatial difference map of T1 (left image) and PD (right image) before registration, (b) is the registration result, T1 (left image) Figure ) and PD (right panel) schematic.
图4是分类器的训练流程图。 Figure 4 is a flow chart of classifier training.
图5是分类器优化过程图。 Figure 5 is a diagram of the classifier optimization process.
图6是斑块成分识别的流程图。 Figure 6 is a flowchart of plaque component identification.
具体实施方式 Detailed ways
下面结合附图对本发明作进一步描述。 The present invention will be further described below in conjunction with the accompanying drawings.
参照图1~图6,一种基于多特征学习的动脉粥样硬化斑块成分分割方法,包括如下步骤: Referring to Figures 1 to 6, a method for segmenting atherosclerotic plaque components based on multi-feature learning includes the following steps:
1)获取多序列动脉血管斑块图像 1) Obtain multi-sequence arterial plaque images
在1.5T磁场强度下采集的T1(T1加权成像)、T1GD(T1造影剂成像)、T2(T2加权成像)、PD(质子密度成像)、STIR(短翻转时间翻转恢复成像)序列的颈动脉(artery vessel)磁共振图像为例; Carotid arteries of T1 (T1 weighted imaging), T1GD (T1 contrast agent imaging), T2 (T2 weighted imaging), PD (proton density imaging), STIR (short inversion time inversion recovery imaging) sequences acquired at a magnetic field strength of 1.5T (artery vessel) magnetic resonance image as an example;
2)图像预处理 2) Image preprocessing
由于采集到图像是整个动脉所在位置的横切面图像,其中动脉血管所在的位置只占到整个图像的2%左右,如果对整幅图像做预处理不仅会造成动脉血管周围的组织对血管内部组织的影响,同时也会增加运算量降低算法处理的效率。为此在图像预处理第一步则是选取以动脉血管内壁为中心60*60 pixel的ROI(Region of Interest)作为研究区域。对选取的ROI做灰度值的窗宽、窗位调整,使其灰度范围分布在(0--255)如式(1), Since the collected image is a cross-sectional image of the entire artery, the location of the artery only accounts for about 2% of the entire image. At the same time, it will increase the amount of computation and reduce the efficiency of algorithm processing. Therefore, the first step in image preprocessing is to select a 60*60 pixel ROI (Region of Interest) centered on the inner wall of the artery as the research area. Adjust the window width and window level of the gray value of the selected ROI so that the gray range is distributed between (0--255) as in formula (1),
然后对ROI做去噪处理,本发明采用的去噪算是基于小波分解的自适应阈值调节的去噪方法。其处理结果如图2所示,图2(a)代表T1序列图像,其中的矩形框是提取的ROI区域,图2(b)是对ROI区域去噪后的结果。 Then the ROI is denoised, and the denoising adopted in the present invention is a denoising method based on wavelet decomposition and adaptive threshold adjustment. The processing results are shown in Figure 2. Figure 2(a) represents a T1 sequence image, in which the rectangular frame is the extracted ROI area, and Figure 2(b) is the result of denoising the ROI area.
在多序列图像成像过程中由于病人的呼吸、脉动、吞咽动作、以及细微的颤抖等都会引起多序列图像刚性和非刚性的形变,这些不可控“运动”所带来的干扰对于像素级别的颈动脉粥样硬化斑块成分识别是致命的。例如病人在现实空间中1mm的移动会引起图像上十到几十个像素点的偏移。因此对序列图像之间的配准工作是必须的。本发明采用了elastix V4.7图像配准的开源软件完成配准工作,其配准结果如图3所示,图3(a)代表T1(左)与PD(右)序列图像配准之前的差异,其中两个不规则的圆代表的是参考图像的血管内外壁,图3(b)表示配准后的结果,明显可以看到T1与PD序列血管内外壁实现了高度吻合; During the multi-sequence image imaging process, due to the patient's respiration, pulsation, swallowing movements, and subtle tremors, etc., the multi-sequence images will cause rigid and non-rigid deformations. Identification of atherosclerotic plaque components is lethal. For example, a 1mm movement of a patient in real space will cause a shift of ten to dozens of pixels on the image. Therefore, the registration work between the sequence images is necessary. The present invention uses the open source software of elastix V4.7 image registration to complete the registration work, and its registration result is shown in Figure 3, Figure 3 (a) represents the T1 (left) and PD (right) sequence image registration before The difference, where the two irregular circles represent the inner and outer walls of the blood vessel in the reference image, Figure 3(b) shows the result after registration, it can be clearly seen that the inner and outer walls of the blood vessel in T1 and PD sequences are highly consistent;
3)斑块特征提取及描述 3) Plaque feature extraction and description
为了能够准确、有效的描述的动脉粥样硬化斑块的特异性,本方法分别从斑块图像的灰度值、高斯模糊特征、梯度值、斑块到血管内外壁的最近距离、图像的拉普拉斯特征以及斑块的纹理特征方面分别描述斑块的特征。以下说明这些特征的提取方法。 In order to accurately and effectively describe the specificity of atherosclerotic plaques, this method separately analyzes the gray value of the plaque image, Gaussian blur feature, gradient value, the shortest distance from the plaque to the inner and outer walls of the blood vessel, and the pull-out of the image. The characteristics of plaques are described in terms of Plath features and texture features of plaques, respectively. The extraction method of these features will be described below.
(a)灰度值 (a) gray value
灰度值即在图像经过去噪处理后的灰度值,这种特征是多序列图像对斑块特异性的直接体现,在一定程度上是最具价值的特征,也因为其获取简单,效果直观而被广泛的应用。 The gray value is the gray value after the image has been denoised. This feature is a direct reflection of the specificity of multi-sequence images for plaques. To a certain extent, it is the most valuable feature. Intuitive and widely used.
(b)高斯模糊特征 (b) Gaussian blur feature
本方法中的高斯模糊特征指的是斑块组织某一点经过高斯模糊之后的灰度值,在一定程度上排除了成像噪声的干扰,其提取过程是采用一个5*5的高斯核与斑块组织做卷积,如式(2)所示。 The Gaussian blur feature in this method refers to the gray value of a certain point of the plaque tissue after Gaussian blurring, which eliminates the interference of imaging noise to a certain extent. The extraction process uses a 5*5 Gaussian kernel and plaque The organization performs convolution, as shown in formula (2).
其中,σ表示高斯核的尺度,x表示图像的灰度值,I,G分别代表图像原始血管斑块图像和高斯模糊后的图像,“*”是卷积符号; Among them, σ represents the scale of the Gaussian kernel, x represents the gray value of the image, I and G represent the original vascular plaque image and the image after Gaussian blur respectively, and "*" is the convolution symbol;
(c)梯度特征 (c) Gradient features
斑块组织的梯度特征GM: Gradient feature GM of plaque tissue:
(d)拉普拉斯特征L: (d) Laplacian feature L:
(e)斑块空间位置特征(到内外壁之间的距离) (e) Spatial location characteristics of plaques (distance to inner and outer walls)
Dmin(I)=min||C(I(x))-Lumen(I)|| (5) D min (I)=min||C(I(x))-Lumen(I)|| (5)
Dmax(I)=max||C(I(x))-Lumen(I)|| (6) D max (I)=max||C(I(x))-Lumen(I)|| (6)
(f)纹理特征 (f) Texture features
对动脉粥样硬化斑块的纹理特征进行提取并分析,有助于对斑块类型进行客观的分类,从而使相关的诊断更加客观和准确。为了能够反映斑块内部灰度分布的统计规律,本方法提取了一阶统计量和灰度共生矩阵(gray level co-occurrence matrix,GLCM)特征两类纹理特征。共七个一阶统计量,其中包括斑块内部灰度均值(Mean)、标准差(STD)、差异系数(coefficient of variance,CoV)、偏度(skewness,Skew)、峰度(kurtosis,Kurtf),另外还有直方图熵(EtHis)如式(7)、亮度熵(EtBrt)如式(8),它们的计算公式分别为: Extracting and analyzing the texture features of atherosclerotic plaques can help to classify the plaque types objectively, so as to make the related diagnosis more objective and accurate. In order to reflect the statistical law of the gray level distribution inside the plaque, this method extracts two types of texture features, the first-order statistics and the gray level co-occurrence matrix (GLCM) feature. A total of seven first-order statistics, including the mean gray value (Mean), standard deviation (STD), coefficient of variance (CoV), skewness (skewness, Skew), kurtosis (kurtosis, Kurtf ), and histogram entropy (EtHis) such as formula (7), brightness entropy (EtBrt) such as formula (8), and their calculation formulas are:
其中,Pi(i=0,1,...,255)是灰阶i在图像中出现的概率,ωi是归一化后不为0的像素强度,N是原图像的像素总数; Wherein, P i (i=0, 1, ..., 255) is the probability that grayscale i appears in the image, ω i is the pixel intensity that is not 0 after normalization, and N is the total number of pixels in the original image;
4)分类器训练 4) Classifier training
分类器训练的目的是一种对问题真实模型的逼近,本方法用到的分类器模型是支持向量机(SVM),该方法是一种经典的机器学习方法,是建立在统计学习理论的VC维理论和结构风险最小原理基础上,根据有限的样本信息在模型的复杂性(即对特定训练样本的学习精度)和学习能力(即无错误地识别任意样本的能力)之间寻求最佳折衷,以期获得最好的推广能力(或称泛化能力)。 The purpose of classifier training is an approximation to the real model of the problem. The classifier model used in this method is support vector machine (SVM). This method is a classic machine learning method and is based on the VC of statistical learning theory. Based on the dimensional theory and the principle of structural risk minimization, according to the limited sample information, the optimal compromise is sought between the complexity of the model (i.e. the learning accuracy for a specific training sample) and the learning ability (i.e. the ability to identify any sample without error). , in order to obtain the best generalization ability (or generalization ability).
分类器的训练样本是从总体样本中随机选取其中的1/2,剩余的1/2 是用来检验分类性能和优化分类器。 The training samples of the classifier are 1/2 randomly selected from the overall samples, and the remaining 1/2 are used to test the classification performance and optimize the classifier.
分类器的训练流程如附图4所示,该过程主要分为以下几个步骤: The training process of the classifier is shown in Figure 4, and the process is mainly divided into the following steps:
(4.1)训练样本的收集与标定 (4.1) Collection and calibration of training samples
训练样本采用的主要是T1(T1加权成像)、T1GD(T1造影剂成像)、T2(T2加权成像)、PD(质子密度成像)、STIR(短翻转时间翻转恢复成像)等五种MRI序列,其中T1序列图像作为参考图像(T1GD,T2,PD,STIR序列配准以T1为参考),另外,医生手动标定动脉粥样硬化斑块成分也是在T1图像上操作的,其他四种序列斑块成分是按照T1标定的结果映射在其上。 The training samples mainly use five MRI sequences, including T1 (T1 weighted imaging), T1GD (T1 contrast agent imaging), T2 (T2 weighted imaging), PD (proton density imaging), and STIR (short inversion time inversion recovery imaging). The T1 sequence image is used as a reference image (T1GD, T2, PD, STIR sequence registration uses T1 as a reference). In addition, the manual calibration of atherosclerotic plaque components by doctors is also performed on the T1 image. The other four sequence plaques The components are mapped on it according to the results of T1 calibration.
(4.2)样本预处理要完成的工作是对样本图像以及医生对样本标定结果的量化表示,该过程的首先对样本图像做灰度值拉伸操作,使其灰度值分布范围在[0,255]区间范围,然后是对图像去噪声处理,最后是把医生的对斑块成分的标定结果做二值化处理,即把医生标定的斑块的每一种成分储存在的二值矩阵中。 (4.2) The work to be done in the sample preprocessing is to quantify the sample image and the calibration results of the sample by the doctor. In this process, the gray value stretching operation is first performed on the sample image so that the gray value distribution range is [0,255] Interval range, then denoise the image, and finally binarize the doctor’s calibration results of the plaque components, that is, store each component of the plaque calibrated by the doctor in the binary matrix.
(4.3)训练样本进一步分类和特征量化处理 (4.3) Further classification and feature quantization processing of training samples
训练样本的进一步分类是把样本细分为lipid(脂肪)样本集、fibrous(纤维)样本集、calcium(钙化)样本集以及hemorrhage(血栓)样本集。 The further classification of the training samples is to subdivide the samples into a lipid (fat) sample set, a fibrous (fiber) sample set, a calcium (calcification) sample set, and a hemorrhage (thrombus) sample set.
特征量化处理是把已经提取到的斑块特征做归一化处理,其目的是使提取到的所有特征量化后的分布范围在[-1,1]区间,因为不同的特征分量的特征对象不同在数量级上有较大差别,然而在SVM的线性分类函数中大的特征分量比小的特征分量影响更大,但是这并不能说明大的特征更加重要,所有必要对特征进行归一化统一,如式(9)所示。 The feature quantization process is to normalize the extracted plaque features. The purpose is to make the distribution range of all extracted features quantized in the [-1,1] interval, because the feature objects of different feature components are different. There is a large difference in magnitude. However, in the linear classification function of SVM, the large feature component has a greater influence than the small feature component, but this does not mean that the large feature is more important, so it is necessary to normalize and unify the features. As shown in formula (9).
其中,xi是特征分量数值,代表特征分量x的平均值,N为训练样本的个数; Among them, x i is the characteristic component value, Represents the average value of the feature component x, N is the number of training samples;
所有特征经过归一化之后形成一个特征向量,该特征向量如式(10)所示。 All features are normalized to form a feature vector, the feature vector As shown in formula (10).
式中V表示斑块的灰度值,G表示斑块的高斯模糊值,GM表示灰度值高斯模糊后的灰度特征,L表示拉普拉斯特征,D表示斑块空间位置特征,T表示斑块的纹理特征。 In the formula, V represents the gray value of the plaque, G represents the Gaussian blur value of the plaque, GM represents the gray feature after the Gaussian blur of the gray value, L represents the Laplacian feature, D represents the spatial position feature of the plaque, and T Represents the texture features of the plaque.
(4.4)分类器训练,最后四种序列MRI动脉斑块图像共生成4个特征集,每一个特征集可以训练生成一个分类器,最终得到4个分类器,即lipid分类器,fibrous分类器,calcium分类器,hemorrhage分类器。 (4.4) Classifier training, the last four kinds of sequence MRI arterial plaque images generate 4 feature sets altogether, each feature set can be trained to generate a classifier, finally get 4 classifiers, i.e. lipid classifier, fibrous classifier, calcium classifier, hemorrhage classifier.
分类器的训练实际上是凸优化问题,通过训练样本优化出一个超平面H: The training of the classifier is actually a convex optimization problem, and a hyperplane H is optimized through training samples:
ω·x+b=0 (11) ω·x+b=0 (11)
其中,ω为权重向量,x为特征向量,b为一参数,该超平面以最大边界的形式将正负样本区分开。 Among them, ω is a weight vector, x is a feature vector, b is a parameter, and the hyperplane distinguishes positive and negative samples in the form of the largest boundary.
5)分类器优化 5) Classifier optimization
分类器优化过程分别得到的4个分类器(lipid,fibrous,calcium,hemorrhage)做优化,其优化的流程如图5所示,下面用对lipid分类器的优化过程为例说明优化过程。 The four classifiers (lipid, fibrous, calcium, and hemorrhage) obtained during the classifier optimization process are optimized, and the optimization process is shown in Figure 5. The optimization process of the lipid classifier is used as an example to illustrate the optimization process.
首先,把样本集分为训练集和测试集两个部分,这两部分的选取是随机的,两者各占50%,然后用训练集的样本训练分类器,用得到的分类器检验测试集的识别率,依次判断循环下去,直到得到的lipid训练器分类正确率达到最大值为止,此时得到的lipid分类器认为是lipid最优分类器。其他成分分类器用同样的方法优化得到。 First, the sample set is divided into two parts, the training set and the test set. The selection of these two parts is random, each accounting for 50%. Then the classifier is trained with the samples of the training set, and the test set is tested with the obtained classifier. The recognition rate is judged successively until the classification accuracy rate of the obtained lipid trainer reaches the maximum value. At this time, the obtained lipid classifier is considered to be the optimal classifier of lipid. Other component classifiers are optimized in the same way.
6)斑块成分识别与形态学操作 6) Plaque component identification and morphological manipulation
斑块成分识别是该方法的核心部分,在本方法中对于斑块组织的某一个像素分别用四个分类器计算其隶属于不同组织的程度,其过程如图6所示。 Plaque component identification is the core part of this method. In this method, four classifiers are used to calculate the degree of belonging to different tissues for a certain pixel of plaque tissue. The process is shown in Figure 6.
(6.1)首先把T1,T1GD,PD,STIR斑块组织图像在每个像素点出的特征提取出来,例如对应于像素点P的纹理特征包括P点在T1,T1GD,PD,STIR四种序列中的纹理特征T(P)=[T1(P),T2(P),T3(P),T4(P)]分别提取到。同样方法提取其他五种特征。 (6.1) First extract the features of each pixel in the T1, T1GD, PD, STIR plaque tissue images, for example, the texture features corresponding to the pixel point P include four sequences of P points in T1, T1GD, PD, STIR The texture features in T(P)=[T 1 (P), T 2 (P), T 3 (P), T 4 (P)] are extracted respectively. The other five features are extracted in the same way.
(6.2)把(6.1)中提取的斑块特征分别放到步骤(5)中优化后的四个分类器中,四种分类器分别计算其隶属于其成分的程度。 (6.2) Put the plaque features extracted in (6.1) into the four classifiers optimized in step (5), and the four classifiers respectively calculate the degree to which they belong to their components.
(6.3)根据得到的(6.2)中的隶属度竞争排序,把隶属度最高的成分作为最终结果。 (6.3) According to the competitive ranking of the membership degree obtained in (6.2), take the component with the highest membership degree as the final result.
7)结果的形态学操作 7) Morphological operation of the result
目的是为了排除噪声对分割结果的影响,排除孤立的组织成分点以及分割结果中空洞等。具体操作如下: The purpose is to eliminate the influence of noise on the segmentation results, to exclude isolated tissue components and holes in the segmentation results. The specific operation is as follows:
(7.1)把成分分割的结果存储在4个二值图像中,二值图像中1代表组织成分,0代表背景,分别是lipid成分、fibrous成分、calcium成分,hemorrhage成分。 (7.1) The result of component segmentation is stored in four binary images. In the binary image, 1 represents the tissue component, and 0 represents the background, which are the lipid component, fibrous component, calcium component, and hemorrhage component.
(7.2)然后对每一个二值图像做腐蚀运算,把孤立的分割块(像素个数小于等于十)滤除,因为太小的成分块对医生诊断是一种干扰,另外这种成分极有可能是图像噪声引起的, (7.2) Then perform corrosion operations on each binary image, and filter out isolated segmentation blocks (the number of pixels is less than or equal to ten), because too small component blocks are a kind of interference to the doctor's diagnosis, and this component is extremely harmful. May be caused by image noise,
(7.3)然后做形态学开运算,开运算的目的在于填补成分块中的空洞以及连通相近的孤立块,增加其连通性。 (7.3) Then do the morphological opening operation. The purpose of the opening operation is to fill the holes in the component blocks and connect similar isolated blocks to increase their connectivity.
(7.4)最后做图像闭运算,该步骤是为了抵消开运算对组织成分的膨胀作用。 (7.4) Finally, the image closing operation is performed, which is to offset the expansion effect of the opening operation on the tissue components.
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