CN113570628A - Leukocyte segmentation method based on active contour model - Google Patents
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Abstract
本发明公开了一种基于活动轮廓模型的白细胞分割方法,由以下步骤组成:步骤1:获得每个小立方体中所有像素值的数量,将每个小立方体中的所有像素值赋值为预定像素值,并获得预处理图像,步骤2:对预处理图像利用密度峰值聚类分割方法进行密度聚类得到初始白细胞图像,然后再使用形态学处理方法对初始白细胞图像中的白细胞核边缘轮廓进行光滑处理得到最终白细胞核图像,步骤3:将最终白细胞核图像利用水平集方法处理得到白细胞核轮廓曲线,以白细胞核轮廓曲线作为活动轮廓模型的初始轮廓曲线,从而得到最终白细胞图像;本发明根据白细胞局部灰度特性,以细胞核轮廓线演化至细胞质边缘分割白细胞,得到更加精准的结果。
The invention discloses a white blood cell segmentation method based on an active contour model, which consists of the following steps: Step 1: Obtain the number of all pixel values in each small cube, and assign all pixel values in each small cube as predetermined pixel values , and obtain the preprocessing image, step 2: Use the density peak clustering segmentation method to perform density clustering on the preprocessing image to obtain the initial white blood cell image, and then use the morphological processing method to smooth the edge contour of the white blood cell nucleus in the initial white blood cell image. Obtain the final leukocyte nucleus image, step 3: use the level set method to process the final leukocyte nucleus image to obtain the leukocyte nucleus contour curve, and use the leukocyte nucleus contour curve as the initial contour curve of the active contour model to obtain the final leukocyte image; Grayscale feature, segment leukocytes with the evolution of the nucleus contour line to the cytoplasmic edge to obtain more accurate results.
Description
技术领域technical field
本发明属于医学图像处理领域,尤其涉及一种基于活动轮廓模型的白细胞分割方法。The invention belongs to the field of medical image processing, in particular to a white blood cell segmentation method based on an active contour model.
背景技术Background technique
医学图像分割由于其对辅助医疗、临床医学等领域的巨大帮助成为了目前图像处理领域的主要研究方向之一。对于特定医学图像,如外周血细胞图像中的白细胞图像。白细胞数目的变化可以作为判断人体是否受到感染。但是通常由于白细胞自身特性或设备影响,使得在染色与图像采集阶段其细胞核和与细胞质不可避免的受到了灰度不均和噪声的影响,尽管不断有分割算法被提出来,但是单一的传统分割算法精度与速度仍然不够理想。Medical image segmentation has become one of the main research directions in the field of image processing due to its great help to the fields of auxiliary medicine and clinical medicine. For certain medical images, such as images of white blood cells in peripheral blood cell images. Changes in the number of white blood cells can be used to determine whether the human body is infected. However, usually due to the characteristics of leukocytes or the influence of equipment, the nucleus and cytoplasm of leukocytes are inevitably affected by uneven grayscale and noise during the stage of staining and image acquisition. Algorithm accuracy and speed are still not ideal.
外周血液图像中除了各类白细胞外,还包含了大量的红细胞、血小板等物质。通常对白细胞采用标准染色法之后虽然能很好的标记出白细胞,但是由于白细胞自身特性,导致其细胞核染色较深,呈深紫色、细胞质部分呈淡紫色差异过大。同时由于背景中红细胞的干扰、染色泄露等问题存在,使得大多数分割算法对于白细胞核的分割性能优秀,但是对于白细胞质的分割效果不尽人意。In addition to various types of white blood cells, peripheral blood images also contain a large number of red blood cells, platelets and other substances. Usually, the standard staining method for leukocytes can mark leukocytes well, but due to the characteristics of leukocytes, the nuclei of leukocytes are deeply stained, dark purple, and the cytoplasm is lavender. At the same time, due to the interference of red blood cells in the background, staining leakage and other problems, most segmentation algorithms have excellent segmentation performance for leukocyte nuclei, but the segmentation effect for leukocyte cytoplasm is unsatisfactory.
通常情况下,白细胞的识别分类系统包括一下几个方面:图像的预处理、白细胞的分割、特征提取与分类。分割作为分类任务之前的环节,对分类的准确性至关重要。目前主要的分割方法有两方面的:其一是基于图谱理论的分割方法,主要有阈值法、聚类法等;其二是基于变分理论的分割方法,主要包括活动轮廓法。基于图谱理论的分割方法目前还没有一种能够有效克服各种情况下白细胞图像灰度不均的问题,而单纯基于变分理论的分割方法,因为白细胞细胞核与细胞质的巨大灰度差,导致活动轮廓的演化曲线难以拟合细胞质边缘或者会收敛于细胞核边缘,导致白细胞难以分割。Usually, the identification and classification system of leukocytes includes the following aspects: image preprocessing, leukocyte segmentation, feature extraction and classification. Segmentation, as a link before the classification task, is crucial to the accuracy of classification. There are two main segmentation methods at present: one is the segmentation method based on the graph theory, mainly including the threshold method, clustering method, etc.; the other is the segmentation method based on the variation theory, mainly including the active contour method. At present, there is no segmentation method based on the atlas theory that can effectively overcome the problem of uneven grayscale of leukocyte images in various situations. However, the segmentation method based solely on variational theory, because of the huge grayscale difference between the leukocyte nucleus and the cytoplasm, leads to activity The evolution curve of the contour is difficult to fit the edge of the cytoplasm or converges to the edge of the nucleus, which makes it difficult to segment the leukocytes.
发明内容SUMMARY OF THE INVENTION
本发明的目的是提供一种基于活动轮廓模型的白细胞分割方法,能够准确分割白细胞细胞核与细胞质。The purpose of the present invention is to provide a leukocyte segmentation method based on an active contour model, which can accurately segment leukocyte nucleus and cytoplasm.
本发明采用以下技术方案:一种基于活动轮廓模型的白细胞分割方法,由以下步骤组成:The present invention adopts the following technical scheme: a leukocyte segmentation method based on an active contour model, which consists of the following steps:
步骤1:将原始染色白细胞图像的RGB像素空间分割为长度相等的小立方体,计算所有小立方体中位于中心点的像素值,即预定像素值,获得每个小立方体中所有像素值的数量,将每个小立方体中的所有像素值赋值为预定像素值,并获得预处理图像,Step 1: Divide the RGB pixel space of the original stained white blood cell image into small cubes of equal length, calculate the pixel value at the center point in all the small cubes, that is, the predetermined pixel value, obtain the number of all pixel values in each small cube, and set the All pixel values in each small cube are assigned to predetermined pixel values, and a preprocessed image is obtained,
步骤2:对所述预处理图像利用密度峰值聚类分割方法进行密度聚类得到初始白细胞图像,然后再使用形态学处理方法对初始白细胞图像中的白细胞核边缘轮廓进行光滑处理得到最终白细胞核图像,Step 2: Perform density clustering on the preprocessed image using the density peak clustering segmentation method to obtain an initial leukocyte image, and then use a morphological processing method to smooth the edge contours of leukocyte nuclei in the initial leukocyte image to obtain a final leukocyte nucleus image ,
步骤3:将最终白细胞核图像利用水平集方法处理得到白细胞核轮廓曲线,以白细胞核轮廓曲线作为活动轮廓模型的初始轮廓曲线,以局部JS散度构建局部灰度信息表达式作为边缘停止函数,使初始轮廓曲线逐步计算得到白细胞边缘,从而得到最终白细胞图像。Step 3: The final leukocyte nucleus image is processed by the level set method to obtain the leukocyte nucleus contour curve, the leukocyte nucleus contour curve is used as the initial contour curve of the active contour model, and the local JS divergence is used to construct the local gray information expression as the edge stop function. The initial contour curve is gradually calculated to obtain the leukocyte edge, thereby obtaining the final leukocyte image.
进一步地,步骤3中所述边缘停止函数为:Further, the edge stop function described in step 3 is:
其中I(x)表示局部图像区域,C1和C2分别为局部区域中被轮廓曲线分为内、外两部分的平均灰度值,p和q分别表示被轮廓曲线分为内外两部分的灰度分布,JS(p,q)表示内、外两部分的灰度分布之间的JS散度信息,α为局部阈值,取0.02,用以衡量局部灰度的均匀性,α-JS(p,q)>0,则表示灰度均匀,反之则表示灰度不均匀,局部窗口选取为5*5。where I(x) represents the local image area, C 1 and C 2 are the average gray values of the local area divided into inner and outer parts by the contour curve, respectively, p and q respectively represent the inner and outer parts divided by the contour curve Gray distribution, JS(p, q) represents the JS divergence information between the gray distribution of the inner and outer parts, α is the local threshold, which is taken as 0.02 to measure the uniformity of the local gray, α-JS ( p, q)>0, it means that the grayscale is uniform, otherwise, it means that the grayscale is not uniform, and the local window is selected as 5*5.
进一步地,步骤2由以下步骤组成:Further, step 2 consists of the following steps:
步骤21:首先对预处理图像逐像素计算其局部密度ρ与相对距离δ,并确定截断距离dc;Step 21: First, calculate the local density ρ and the relative distance δ of the preprocessed image pixel by pixel, and determine the truncation distance d c ;
其中,密度聚类的局部密度ρ为:Among them, the local density ρ of density clustering is:
上式中,di,j表示像素点i和像素点j之间的距离,使用欧几里得距离计算,dcutoff表示截断距离,即有效密度半径,它是密度峰值聚类中唯一的参数,取最大相对距离的0.5%,那么ρi就可以表示像素点周围像素点i的分布情况,即局部密度;In the above formula, d i, j represent the distance between pixel i and pixel j, calculated using Euclidean distance, d cutoff represents the cutoff distance, that is, the effective density radius, which is the only parameter in the density peak clustering , take 0.5% of the maximum relative distance, then ρ i can represent the distribution of pixel i around the pixel, that is, the local density;
其中,密度聚类的相对距离δ为:Among them, the relative distance δ of density clustering is:
步骤22:将所有像素点局部密度ρ与相对距离δ的乘积降序排列,取前N个点为聚类中心点,聚类中心N值取2,表示聚类结构分两类,一类为背景部分,一类为前景白细胞核;Step 22: Arrange the product of the local density ρ of all pixel points and the relative distance δ in descending order, take the first N points as the cluster center point, and the cluster center N value is 2, indicating that the cluster structure is divided into two categories, one is the background part, one is the foreground leukocyte nucleus;
步骤23:将剩余的点,即除N个聚类中心点以外的像素点,根据聚类中心进行归类划分,得到聚类完成的图像,即白细胞核图像,将聚类得到的白细胞核图像二值化,白细胞核作为前景,其余部分作为背景,使用形态学处理方法,利用形态学膨胀与腐蚀运算消除细小区域;处理前景部分,获取白细胞核边缘轮廓,再对白细胞核边缘轮廓进行光滑处理,即对图像利用高斯滤波平滑其边缘,Sigma取1.8,得到最终白细胞核图像。Step 23: Classify and divide the remaining points, that is, pixels other than the N cluster center points, according to the cluster centers, and obtain the clustered image, that is, the white blood cell nucleus image, and the white blood cell nucleus image obtained by clustering is obtained. Binarization, the white blood cell nucleus is used as the foreground, and the rest is used as the background. The morphological processing method is used, and the small area is eliminated by morphological expansion and erosion operations; the foreground part is processed, and the edge contour of the white blood cell nucleus is obtained. That is, the image is smoothed by Gaussian filtering, and the Sigma is taken as 1.8 to obtain the final white blood cell nucleus image.
本发明的有益效果是:本发明通过结合图谱理论的聚类法与差分理论的活动轮廓法分割白细胞图像,通过密度聚类得到白细胞核的粗轮廓,同时考虑白细胞的自身特征,根据白细胞局部灰度特性,以细胞核轮廓线演化至细胞质边缘分割白细胞,得到更加精准的结果。The beneficial effects of the present invention are as follows: the present invention divides the leukocyte image by combining the clustering method of the atlas theory and the active contour method of the difference theory, obtains the rough outline of the leukocyte nucleus through density clustering, and considers the characteristics of the leukocyte itself. The leukocytes are segmented by the evolution of the contour of the nucleus to the edge of the cytoplasm, and more accurate results are obtained.
附图说明Description of drawings
图1为本发明实施例1中的染色白细胞图像步骤1的处理前后结果;图1a为预处理前图像,图1b为预处理后图像,图1c为预处理后图像;Fig. 1 shows the results before and after the processing of step 1 of the stained leukocyte image in Example 1 of the present invention; Fig. 1a is an image before preprocessing, Fig. 1b is an image after preprocessing, and Fig. 1c is an image after preprocessing;
图2为本发明实施例1中对白细胞图像进行密度峰值聚类分割白细胞核的处理前后结果,图2a为密度聚类后获得的白细胞核图像,图2b为进行二值化,形态学处理、光滑后的白细胞核图像;Figure 2 shows the results before and after the processing of leukocyte nuclei by density peak clustering on leukocyte images in Example 1 of the present invention, Figure 2a shows the leukocyte nuclei images obtained after density clustering, Figure 2b shows binarization, morphological processing, Smoothed leukocyte nuclei images;
图3为本发明实施例1中对白细胞图像采用活动轮廓法精细分割的结果,图3a为细胞核轮廓初始化为初始轮廓线图像,图3b为曲线演化结果,图3c为最终分割结果。Figure 3 is the result of fine segmentation of the white blood cell image by the active contour method in Example 1 of the present invention, Figure 3a is the initial contour image of the cell nucleus, Figure 3b is the curve evolution result, and Figure 3c is the final segmentation result.
具体实施方式Detailed ways
下面结合附图和具体实施方式对本发明进行详细说明。The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
本发明公开了一种基于活动轮廓模型的白细胞分割方法,由以下步骤组成:The invention discloses a leukocyte segmentation method based on an active contour model, which consists of the following steps:
步骤1:将原始染色白细胞图像的RGB像素空间分割为长度相等的小立方体,计算所有小立方体中位于中心点的像素值,即预定像素值,获得每个小立方体中所有像素值的数量,将每个小立方体中的所有像素值赋值为预定像素值,并获得预处理图像。Step 1: Divide the RGB pixel space of the original stained white blood cell image into small cubes of equal length, calculate the pixel value at the center point in all the small cubes, that is, the predetermined pixel value, obtain the number of all pixel values in each small cube, and set the All pixel values in each small cube are assigned to predetermined pixel values, and a preprocessed image is obtained.
由于对原始白细胞图像进行密度聚类处理的话,将要面对庞大的数据处理问题,所以首先对白细胞图像采用mini-RGBCUBE方式进行预处理,降低像素数量。Since the density clustering of the original leukocyte image will face huge data processing problems, the mini-RGBCUBE method is used to preprocess the leukocyte image first to reduce the number of pixels.
步骤2:对预处理图像利用密度峰值聚类分割方法进行密度聚类得到初始白细胞图像,然后再使用形态学处理方法对初始白细胞图像中的白细胞核边缘轮廓进行光滑处理得到最终白细胞核图像。Step 2: Use the density peak clustering segmentation method to perform density clustering on the preprocessed image to obtain the initial white blood cell image, and then use the morphological processing method to smooth the edge contour of the white blood cell nucleus in the initial white blood cell image to obtain the final white blood cell nucleus image.
除了步骤2中的密度峰值聚类分割方法,当然可以采用其他分割方法,如K-Means(K均值)、C均值等分割方法,但是综合考虑权衡分割小区域的颜色一致性以及算法的时间复杂度,密度峰值聚类算法性能相对最优。In addition to the density peak clustering segmentation method in step 2, other segmentation methods can of course be used, such as K-Means (K-means), C-means and other segmentation methods, but the color consistency of the segmentation small area and the time complexity of the algorithm are comprehensively considered. The performance of the density peak clustering algorithm is relatively optimal.
对步骤1中预处理彩色图像进行密度聚类,采用欧式距离度量,将聚类结果二值化,白细胞核作为前景,其余部分作为背景;然后再使用形态学处理方法对初始白细胞图像中的白细胞核边缘轮廓进行光滑处理得到最终白细胞核图像。Perform density clustering on the preprocessed color image in step 1, and use Euclidean distance metric to binarize the clustering results, with leukocyte nuclei as the foreground and the rest as the background; The edge contours of the nuclei were smoothed to obtain the final leukocyte nuclear image.
其中,步骤2由以下步骤组成:Among them, step 2 consists of the following steps:
步骤21:首先对预处理图像逐像素计算其局部密度ρ与相对距离δ,并确定截断距离dc。Step 21: First, calculate the local density ρ and the relative distance δ of the preprocessed image pixel by pixel, and determine the cutoff distance d c .
其中,密度聚类的局部密度ρ为:Among them, the local density ρ of density clustering is:
上式中,di,j表示像素点i和像素点j之间的距离,使用欧几里得距离计算。dcutoff表示截断距离,即有效密度半径,它是密度峰值聚类中唯一的参数,取最大相对距离的0.5%。那么ρi就可以表示像素点周围像素点i的分布情况,即局部密度。In the above formula, d i,j represents the distance between pixel i and pixel j, which is calculated using Euclidean distance. d cutoff represents the cutoff distance, that is, the effective density radius, which is the only parameter in the density peak clustering, and takes 0.5% of the maximum relative distance. Then ρ i can represent the distribution of pixel i around the pixel, that is, the local density.
其中,密度聚类的相对距离δ为:Among them, the relative distance δ of density clustering is:
步骤22:将所有像素点局部密度ρ与相对距离δ的乘积降序排列,取前N个点为聚类中心点,聚类中心N值取2,表示聚类结构分两类,一类为背景部分,一类为前景白细胞核。Step 22: Arrange the product of the local density ρ of all pixel points and the relative distance δ in descending order, take the first N points as the cluster center point, and the cluster center N value is 2, indicating that the cluster structure is divided into two categories, one is the background In part, one type is foreground leukocyte nuclei.
步骤23:将剩余的点,即除N个聚类中心点以外的像素点,根据聚类中心进行归类划分,得到聚类完成的图像,即白细胞核图像,将聚类得到的白细胞核图像二值化,白细胞核作为前景,其余部分作为背景,使用形态学处理方法,利用形态学膨胀与腐蚀运算消除细小区域;处理前景部分,获取白细胞核边缘轮廓,再对白细胞核边缘轮廓进行光滑处理,即对图像利用高斯滤波平滑其边缘,Sigma取1.8,得到最终白细胞核图像。Step 23: Classify and divide the remaining points, that is, pixels other than the N cluster center points, according to the cluster centers, and obtain the clustered image, that is, the white blood cell nucleus image, and the white blood cell nucleus image obtained by clustering is obtained. Binarization, the white blood cell nucleus is used as the foreground, and the rest is used as the background. The morphological processing method is used, and the small area is eliminated by morphological expansion and erosion operations; the foreground part is processed, and the edge contour of the white blood cell nucleus is obtained. That is, the image is smoothed by Gaussian filtering, and the Sigma is taken as 1.8 to obtain the final white blood cell nucleus image.
步骤3:将最终白细胞核图像利用水平集方法处理得到细胞核轮廓曲线,以细胞核轮廓曲线作为活动轮廓模型的初始轮廓曲线,以局部JS散度构建局部灰度信息表达式作为边缘停止函数,使初始轮廓曲线逐步计算得到白细胞边缘,从而得到最终白细胞图像。Step 3: The final leukocyte nuclear image is processed by the level set method to obtain the nucleus contour curve, the nucleus contour curve is used as the initial contour curve of the active contour model, and the local JS divergence is used to construct the local gray information expression as the edge stop function, so that the initial The contour curve is calculated step by step to obtain the leukocyte edge, thereby obtaining the final leukocyte image.
其中,步骤3由以下步骤组成:Among them, step 3 consists of the following steps:
步骤31:将最终白细胞核图像利用水平集方法处理得到细胞核轮廓曲线,以细胞核轮廓曲线为初始轮廓线,将步骤24获得的白细胞核边缘轮廓经过水平集法处理,白细胞核部分置1,其余部分置0。Step 31: Use the level set method to process the final leukocyte nucleus image to obtain the nucleus contour curve, take the nucleus contour curve as the initial contour line, process the edge contour of the leukocyte nucleus obtained in step 24 through the level set method, set the leukocyte nucleus part to 1, and set the rest part to 1. Set to 0.
步骤32:构建局部JS散度驱动的边缘停止函数;Step 32: Build a local JS divergence-driven edge stop function;
边缘停止函数为:The edge stop function is:
其中,I(x)表示局部图像区域,C1和C2分别为局部区域中被轮廓曲线分为内、外两部分的平均灰度值。JS(p||q)表示内、外两部分的灰度分布之间的JS散度信息,α为局部阈值,取0.02,用以衡量局部灰度的均匀性。Among them, I(x) represents the local image area, and C1 and C2 are the average gray value of the local area divided into inner and outer parts by the contour curve, respectively. JS(p||q) represents the JS divergence information between the gray distributions of the inner and outer parts, and α is the local threshold, which is taken as 0.02 to measure the uniformity of the local gray.
步骤33:利用边缘停止函数根据白细胞局部灰度变化情况演化活动轮廓曲线得到最终白细胞图像。Step 33: Using the edge stopping function to evolve the active contour curve according to the local gray level change of the white blood cells to obtain the final white blood cell image.
本发明利用活动轮廓曲线演化进行白细胞精细分割,以细胞核轮廓曲线作为活动轮廓模型的初始轮廓曲线,通过以局部JS散度构建局部灰度信息表达式作为边缘停止函数,指导初始轮廓曲线演化至白细胞边缘,从而得到白细胞精确分割图像。The invention uses the evolution of the active contour curve to perform fine segmentation of leukocytes, uses the nucleus contour curve as the initial contour curve of the active contour model, and uses the local JS divergence to construct a local gray information expression as the edge stop function to guide the evolution of the initial contour curve to white blood cells. edge, so as to obtain an accurate segmentation image of white blood cells.
本发明首先对原始白细胞图像进行预处理,然后对细胞核进行粗分割,利用粗分割轮廓进行曲线演化的活动轮廓模型精确分割。一方面根据白细胞图像细胞质与细胞核染色不均的问题进行两步法分割白细胞;另一方面采用具有坚实数学理论依据的变分活动轮廓模型分割法,有效的将数学理论与实际环境相结合,并且获得了活动轮廓模型的边缘停止函数,对灰度不均的白细胞做出了更加精确的分割,既考虑了白细胞核在外周血液图像中的染色特点,通过聚类的方式获得了白细胞核部分,又针对白细胞质部分的灰度不均的特点,采用边缘停止函数来演化轮廓曲线,综合两方面,实现了更加精准的白细胞分割方法。The invention first preprocesses the original white blood cell image, then roughly divides the cell nucleus, and uses the rough segmentation contour to accurately segment the active contour model of the curve evolution. On the one hand, a two-step method is used to segment leukocytes according to the uneven staining of cytoplasm and nuclei in leukocyte images; The edge stopping function of the active contour model is obtained, and the white blood cells with uneven grayscale are more accurately segmented. Considering the staining characteristics of the leukocyte nuclei in the peripheral blood image, the leukocyte nuclei are obtained by clustering. In view of the uneven gray level of the leukocyte, the edge stop function is used to evolve the contour curve, and a more accurate leukocyte segmentation method is realized by combining the two aspects.
实施例1Example 1
步骤1:将输入的原始染色白细胞彩色图像,如图1a利用RGB-CUBE方式进行预处理,降低局部像素数量,将RGB像素空间(256*256*256)划分为每个边长为16的小立方体,定义边长lenminicube=16。Step 1: Preprocess the input original stained white blood cell color image, as shown in Figure 1a, using the RGB-CUBE method to reduce the number of local pixels, and divide the RGB pixel space (256*256*256) into small pixels with a side length of 16. Cube, define side length lenminicube=16.
计算所有小立方体中位于中心点的像素值,即预定像素值,获得每个小立方体中所有像素值的数量npixel,将每个小立方体中的所有像素值赋值为预定像素值,并获得预处理图像,以此降低原始图像像素数量。Calculate the pixel value at the center point in all the small cubes, that is, the predetermined pixel value, obtain the number npixel of all pixel values in each small cube, assign all the pixel values in each small cube to the predetermined pixel value, and obtain the preprocessing image, thereby reducing the number of pixels in the original image.
选择小立方体的长度为2n=16(n=4)中,实际n可以取(0-9),但是随着n取值的不算变大,在把小立方体中的像素点归结到每个小立方体中心后,图像的整体像素会急剧减少,以至于原始图像不可分辨,当n=0时,小立方体长度为1,此时其中心像素就是它自己,相当于对图像未作变化;当n=9时,小立方体长度为256,此时其中心像素为(127,127,127),相当于把整幅图像变为了一种灰度值,如图1b。所以经过实验,选取n=4时,可以兼顾效率与准确度,同时每个小立方体中心点像素的计算方法为:Select the length of the small cube to be 2 n = 16 (n=4), the actual n can be taken as (0-9), but as the value of n does not become larger, the pixels in the small cube are attributed to each After the center of a small cube, the overall pixels of the image will decrease sharply, so that the original image is indistinguishable. When n=0, the length of the small cube is 1, and its center pixel is itself, which is equivalent to no change to the image; When n=9, the length of the small cube is 256, and its center pixel is (127, 127, 127), which is equivalent to turning the entire image into a grayscale value, as shown in Figure 1b. Therefore, after experiments, when n=4 is selected, both efficiency and accuracy can be taken into account. At the same time, the calculation method of the center point pixel of each small cube is:
其中,表示向下取整符号,pixel表示原始图像的像素值。in, Indicates the round-down symbol, and pixel represents the pixel value of the original image.
利用RGBCUBE方法将原始像素数量降低,便于后续聚类处理,由此获得了预处理图像,如图1c。The RGBCUBE method is used to reduce the number of original pixels to facilitate subsequent clustering processing, thus obtaining a preprocessed image, as shown in Figure 1c.
步骤2:对步骤1中预处理图像进行密度聚类,采用欧式距离度量,将聚类结果二值化,白细胞核作为前景(白色),其余部分作为背景(黑色)。然后使用形态学处理方法,处理前景部分,并且采用连通域标记的方式对初始白细胞图像中的白细胞核边缘轮廓进行光滑处理得到最终白细胞核图像。Step 2: Perform density clustering on the preprocessed images in Step 1, and use the Euclidean distance metric to binarize the clustering results. The white blood cell nuclei are used as the foreground (white), and the rest are used as the background (black). Then, the morphological processing method is used to process the foreground part, and the edge contours of leukocyte nuclei in the initial leukocyte image are smoothed by means of connected domain marking to obtain the final leukocyte nucleus image.
针对步骤1中预处理图像得到了每个小立方体中的像素数npixel与其小立方体中心点的像素值centerpoints,然后对每一个中心像素值centerpoints计算其局部密度ρ,其中,密度聚类的局部密度ρ为:For the preprocessed image in step 1, the number of pixels in each small cube npixel and the pixel value centerpoints of the center point of the small cube are obtained, and then the local density ρ is calculated for each center pixel value centerpoints, where the local density of the density clustering ρ is:
上式中,di,j表示像素点i和像素点j之间的距离,使用欧几里得距离计算。dcutoff表示截断距离,即有效密度半径,它是密度峰值聚类中唯一的参数,取最大相对距离的0.5%。经过实验测试证明:dc取最大相对距离的0.5%可以得到更好的聚类结果;那么ρi就可以表示像素点周围像素点i的分布情况,即局部密度。In the above formula, d i, j represents the distance between pixel i and pixel j, which is calculated using Euclidean distance. d cutoff represents the cutoff distance, that is, the effective density radius, which is the only parameter in the density peak clustering, and takes 0.5% of the maximum relative distance. The experimental test proves that: d c takes 0.5% of the maximum relative distance to obtain better clustering results; then ρ i can represent the distribution of pixel i around the pixel, that is, the local density.
其中,密度聚类的相对距离δ为:Among them, the relative distance δ of density clustering is:
计算出每个像素点centerpoints的局部密度与相对距离后,将两者的乘积做降序排列,取前N为2的点作为聚类中心,这里聚类中心个数N的选取,目的是获得白细胞核部分,所以选取聚类个数为2。After calculating the local density and relative distance of each pixel point centerpoints, arrange the product of the two in descending order, and take the points whose first N is 2 as the cluster center. The nucleus part, so the number of clusters is selected as 2.
找到聚类中心后,将其余的像素点进行扩展聚类簇,将其分配到与它最近且局部密度比它大的数据点的簇中,最终得到密度峰值聚类结果,如图2a。对聚类后得到的结果利用形态学膨胀与腐蚀运算消除细小区域,核尺寸均为5X5。对形态学处理完毕的图像进行高斯平滑滤波,平滑边缘,光滑操作也可以采用中值滤波,均值滤波等,但是采用高斯滤波对图像进行平滑的同时,能够更多的保留图像的总体灰度分布特征,得到最终白细胞核图像,如图2b。After the cluster center is found, the remaining pixel points are expanded to cluster, and they are assigned to the cluster of data points that are closest to it and whose local density is larger than it, and finally get the density peak clustering result, as shown in Figure 2a. For the results obtained after clustering, morphological dilation and erosion operations are used to eliminate small areas, and the kernel size is 5X5. Gaussian smoothing filtering, smoothing edges, and smoothing operations can also be performed on the morphologically processed image by using median filtering, mean filtering, etc., but using Gaussian filtering to smooth the image can preserve the overall grayscale distribution of the image. feature to obtain the final leukocyte nuclear image, as shown in Figure 2b.
步骤3:活动轮廓算法精确分割白细胞Step 3: Active Contour Algorithm Precisely Segments WBCs
以白细胞核轮廓曲线为初始水平集,如图3a所示,将步骤2获得的最终白细胞核图像,细胞核部分置1,其余部分置0,然后作为初始水平集函数。Taking the white blood cell nucleus contour curve as the initial level set, as shown in Figure 3a, the final white blood cell nucleus image obtained in step 2, the nucleus part is set to 1, the rest is set to 0, and then used as the initial level set function.
计算局部区域中被轮廓曲线分为内、外两部分的灰度均值C1和C2:Calculate the gray mean values C 1 and C 2 of the inner and outer parts divided by the contour curve in the local area:
其中,I为局部图像,H(φ)为单位阶跃函数的平滑形式,用以计算局部区域的内外部分,具体为:Among them, I is the local image, and H(φ) is the smooth form of the unit step function, which is used to calculate the inner and outer parts of the local area, specifically:
其中,phi为轮廓曲线,eps为一极小常数,取eps=1e-3。Among them, phi is the contour curve, eps is a minimum constant, and eps=1e-3 is taken.
利用局部灰度图像均值,构建局部Jensen-Shannon散度驱动的边缘停止函数:Using the local grayscale image mean, an edge stopping function driven by the local Jensen-Shannon divergence is constructed:
式中,I(x)表示局部图像区域,C1和C2分别为局部区域中被轮廓曲线分为内、外两部分的平均灰度值,p和q分别表示被轮廓曲线分为内外两部分的灰度分布,JS(p,q)表示内、外两部分的灰度分布之间的JS散度信息,α为局部阈值,取0.02,用以衡量局部灰度的均匀性,α-JS(p,q)>0,则表示灰度均匀,反之则表示灰度不均匀,局部窗口选取为5*5。In the formula, I(x) represents the local image area, C 1 and C 2 represent the average gray value of the local area divided into inner and outer parts by the contour curve, respectively, p and q represent the inner and outer parts divided by the contour curve, respectively. Partial grayscale distribution, JS(p, q) represents the JS divergence information between the grayscale distribution of the inner and outer parts, α is the local threshold, which is 0.02 to measure the uniformity of the local grayscale, α- If JS(p, q)>0, it means that the grayscale is uniform, otherwise, it means that the grayscale is not uniform, and the local window is selected as 5*5.
利用构建完成的边缘停止函数指导轮廓曲线演化,假设图像中目标区域灰度密度大于背景区域(反之,同样成立),当JS(p,q)>α,,即α-JS(p,q)<0,gjspf<0,意味着轮廓曲线向着灰度变化大的区域移动,最终处于图像边缘处;当JS(p,q)<α,即α-JS(p,q)>0,gjspf>0意味着演化曲线正处于图像中灰度平坦区,各个像素灰度值相差不大。在轮廓曲线演化时,持续检测gjspf的符号,轮廓曲线不断向着目标边缘靠近,最终停在目标边界处。Using the constructed edge stop function to guide the evolution of the contour curve, it is assumed that the gray density of the target area in the image is greater than that of the background area (and vice versa), when JS(p, q)>α, that is, α-JS(p, q) <0, g jspf <0, which means that the contour curve moves to the area with large grayscale changes, and finally is at the edge of the image; when JS(p, q)<α, that is, α-JS(p, q)>0, g jspf > 0 means that the evolution curve is in the flat gray area of the image, and the gray value of each pixel is not much different. When the contour curve evolves, the symbol of g jspf is continuously detected, and the contour curve keeps approaching the target edge and finally stops at the target boundary.
通过gjspf符号的不断变化,轮廓曲线最终在细胞边缘收敛,此时,轮廓曲线内部的符号为负,轮廓曲线外部的符号为正,这样就达到精确分割白细胞的目的,如图3b与图3c。Through the constant change of the symbol of g jspf , the contour curve finally converges at the cell edge. At this time, the symbol inside the contour curve is negative, and the symbol outside the contour curve is positive, so as to achieve the purpose of accurately segmenting white blood cells, as shown in Figure 3b and Figure 3c .
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。The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection of the present invention. within the range.
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