Author Affiliations
Abstract
1 Innovation Photonics and Imaging Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
2 Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150081, China
3 Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
4 Institute of Optical Measurement and Intellectualization, Harbin Institute of Technology, Harbin 150080, China
5 Beijing Institute of Collaborative Innovation, Beijing 100094, China
6 State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing 100871, China
A critical function of flow cytometry is to count the concentration of blood cells, which helps in the diagnosis of certain diseases. However, the bulky nature of commercial flow cytometers makes such tests only available in hospitals or laboratories, hindering the spread of point-of-care testing (POCT), especially in underdeveloped areas. Here, we propose a smart Palm-size Optofluidic Hematology Analyzer based on a miniature fluorescence microscope and a microfluidic platform to lighten the device to improve its portability. This gadget has a dimension of 35 × 30 × 80 mm and a mass of 39 g, less than 5% of the weight of commercially available flow cytometers. Additionally, automatic leukocyte concentration detection has been realized through the integration of image processing and leukocyte counting algorithms. We compared the leukocyte concentration measurement between our approach and a hemocytometer using the Passing-Bablok analysis and achieved a correlation coefficient of 0.979. Through Bland-Altman analysis, we obtained the relationship between their differences and mean measurement values and established 95% limits of agreement, ranging from ?0.93×103 to 0.94×103 cells/μL. We anticipate that this device can be used widely for monitoring and treating diseases such as HIV and tumors beyond hospitals.
hematology analyzer miniature fluorescence microscope microfluidics leukocyte concentration Opto-Electronic Science
2023, 2(12): 230018
人体内不同白细胞的数值水平是疾病判断的重要依据之一。在白细胞计数中, 准确分割白细胞是后续分类计数的重要前提步骤, 决定了后续分类计数的准确与否。对于使用各种不同染色方法所得的血液涂片图像, 都可以通过颜色校准, 分别调整RGB图像的R、G、B分量, 改变图像的色彩信息, 接着通过图像HSI颜色空间中的H分量, 结合Otsu自动阈值法和面积阈值法对图像进行分割、处理, 最终准确定位、分割提取出整个白细胞, 用于后续的分类计数。实验结果表明, 该方法分割准确度高、实用性强、操作简单并且效率高。
白细胞分割 HSI颜色空间 颜色校准 Otsu法 面积阈值法 leukocyte segmentation HSI color space color calibration Otsu method area threshold method
人体外周血五类白细胞数量和所占比例反映了人体的健康状态, 人工检查白细胞消耗医务人员的大量精力, 如何用智能方法快速、准确进行白细胞智能分类是一个亟待解决的问题, 其中白细胞分割的准确性是正确分类的关键。提出了一种改进的迭代阈值图像分割算法, 对恢复有丝分裂连线的最小距离方法进行了基于数学和数模分析的预处理改良, 提升了白细胞图像分割的精度和效率, 解决了血小板粘连、白细胞边界不分明等问题。将白细胞从复杂的血液环境中分离出来, 对有丝分裂细胞的多叶核进行最小距离判定并连线, 然后定位到每个白细胞, 制作数据集, 最后用CNN分类。经测试, 分割正确率达到了96%以上。实验结果验证了所提分割方法的准确性、高效性和实用性。
白细胞 迭代阈值 彩色空间 图像分割 卷积神经网络 leukocyte iterative threshold color space image segmentation convolutional neural network
血液白细胞的准确计数对诊断血液疾病非常重要，其中白细胞核分割的准确性是正确判定血液白细胞类别的重要预处理步骤。不同的染色条件和不同的照明条件下，血液白细胞的B分量和G分量存在明显的差值，基于Otsu方法阈值处理和面积阈值处理后进行准确的白细胞核分割。通过建立数学模型，基于形态学特征对完整核白细胞和多叶核白细胞进行分类判定，对存在有丝分裂的多叶核白细胞核进行边缘检测，根据最大最小距离判定多叶核白细胞个数，以实现对血液白细胞的准确计数。实验结果验证了所提分割判定方法的可行性和实用性。
图像处理 白细胞计数 白细胞分割 多叶核白细胞 形态学特征 image processing leukocyte count leukocyte segmentation lobulatednucleus leukocytes morphological characteristics
Author Affiliations
Abstract
Shanghai Key Laboratory of Multidimensional, Information Processing, East China Normal University, Shanghai 200241, P. R. China
Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia (ALL). As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution, which significantly affects the absorption and reflection of light, the spectral feature is proved to be important for leukocytes classification and identification. This paper proposes an accurate identification method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging (HSI) technology which combines the spectral information. The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm. Then, the spectral features are extracted and combined with the spatial features. Based on this, the support vector machine (SVM) is applied for classification of five types of leukocytes and abnormal leukocytes. Compared with different classification methods, the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes, improving the accuracy in the classification and identification of leukocytes. This paper only selects one subtype of ALL for test, and the proposed method can be applied for detection of other leukemia in the future.
Leukocyte microscopic hyperspectral imaging nucleus segmentation Acute Lymphoblastic Leukemia Journal of Innovative Optical Health Sciences
2020, 13(2): 2050005
为了实现人体外周血白细胞的快速无重复自动扫描,提出了一种新的聚类方法——视窗聚类法。由于人体外周血白细胞在血液涂片中分布稀疏且不均匀,传统的利用高倍镜直接逐行扫描的人工方法效率比较低下。用高倍物镜有目的地对有白细胞的区域进行拍摄可以提高白细胞的扫描效率。用低倍物镜预扫描血片,由于低倍物镜视场大,在低倍图中,用图像处理的方法可获得白细胞核心坐标。视窗聚类方法首先用最大最小距离聚类方法确定白细胞核心原始聚类中心,再按照高倍镜视窗大小对聚类中心进行调整,在确保各聚类视窗内无重复白细胞存在的同时,尽可能合并原始聚类中心,减少总的聚类中心个数。以新的视窗聚类中心组成高倍物镜扫描路径扫描血片,可保证高倍图像最大程度地包含尽可能多的白细胞,进而减少高倍图像的拍摄次数,实现了白细胞的快速无重复扫描。实验表明,用该方法获取110个无重复的白细胞图像仅需要拍摄高倍图像72~85幅,整个过程大概需要1.5~2.0 min。同时,视窗聚类方法可以在无人监管的情况下完全避免白细胞图像的重复拍摄,实现了人体外周血白细胞的自动扫描。
白细胞 无重复扫描 视窗聚类 最大最小聚类 白细胞自动扫描 leukocyte non-redundant scanning window clustering max-min clustering leukocyte fast scanning
血片镜检可以实现白细胞的分类计数，同时还能提供详细的白细胞形态等特征，有助于疾病的诊断。目前国内大多数医院白细胞检测的主要方法是人工镜检，但人工镜检依赖医务人员的工作经验，劳动强度大，检测效率低。因此提出一种基于RGB彩色空间分量差的白细胞细胞核的快速分割方法。通过显微镜分析人体外周血液涂片的显微图像，发现白细胞细胞核区域的B分量和G分量的差值明显比其他区域大，可以通过一个简单8 bit的B-G运算，来实现五类白细胞细胞核的快速分割，白细胞细胞核的平均分割时间为0.26 ms，体现了较好的鲁棒性和实时性。该方法成功应用到白细胞的实时在线自动扫描镜检中，提高了镜检的效率。
白细胞核的快速分割 显微图像 彩色空间 分量差 白细胞计数 quick leukocyte nucleus segmentation microscopic images color space component difference leukocyte counting
Author Affiliations
Abstract
Department of Optical Electronics Sichuan University, Chengdu Sichuan 610064 P. R. China
A leukocyte image fast scanning method based on max-min distance clustering is proposed. Because of the lower proportion and uneven distribution of leukocytes in human peripheral blood, there will not be any leukocyte in lager quantity of the captured images if we directly scan the blood smear along an ordinary zigzag scanning routine with high power (100x) objective. Due to the larger field of view of low power (10x) objective, the captured low power blood smear images can be used to locate leukocytes. All of the located positions make up a specific routine, if we scan the blood smear along this routine with high power objective, there will be definitely leukocytes in almost all of the captured images. Considering the number of captured images is still large and some leukocytes may be redundantly captured twice or more, a leukocyte clustering method based on max–min distance clustering is developed to reduce the total number of captured images as well as the number of redundantly captured leukocytes. This method can improve the scanning efficiency obviously. The experimental results show that the proposed method can shorten scanning time from 8.0–14.0 min to 2.5–4.0 min while extracting 110 nonredundant individual high power leukocyte images.
Leukocyte image fast scanning scanning routine max–min distance clustering window clustering microscopic imaging image segmentation Journal of Innovative Optical Health Sciences
2016, 9(6): 1650022
Author Affiliations
Abstract
Department of Optical Electronics Sichuan University, Chengdu Sichuan 610064, P. R. China
A leukocyte recognition method for human peripheral blood smear based on island-clustering texture (ICT) is proposed. By analyzing the features of the five typical classes of leukocyte images, a new ICT model is established. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift clustering to be the centers of islands. Secondly, the growing region is employed to create regions of the islands in which the seeds are just these feature points. These islands distribution can describe a new texture. Finally, a distinguished parameter vector of these islands is created as the ICT features by combining the ICT features with the geometric features of the leukocyte. Then the five typical classes of leukocytes can be recognized successfully at the correct recognition rate of more than 92.3% with a total sample of 1310 leukocytes. Experimental results show the feasibility of the proposed method. Further analysis reveals that the method is robust and results can provide important information for disease diagnosis.
Image processing leukocyte recognition texture analysis island-clustering texture Journal of Innovative Optical Health Sciences
2016, 9(1): 1650009
Author Affiliations
Abstract
Department of Optical Electronics Sichuan University, Chengdu Sichuan 610064, P. R. China
A leukocyte segmentation method based on S component and B component images is proposed. Threshold segmentation operation is applied to get two binary images in S component and B component images. The samples used in this study are peripheral blood smears. It is easy to find from the two binary images that gray values are the same at every corresponding pixels in the leukocyte cytoplasm region, but opposite in the other regions. The feature shows that "IMAGE AND" operation can be employed on the two binary images to segment the cytoplasm region of leukocyte. By doing "IMAGE XOR" operation between cytoplasm region and nucleus region, the leukocyte segmentation can be retrieved effectively. The segmentation accuracy is evaluated by comparing the segmentation result of the proposed method with the manual segmentation by a hematologist. Experiment results show that the proposed method is of a higher segmentation accuracy and it also performs well when leukocytes overlap with erythrocytes. The average segmentation accuracy of the proposed method reaches 97.7% for segmenting five types of leukocyte. Good segmentation results provide an important foundation for leukocytes automatic recognition.
Image segmentation leukocyte component image B component image Journal of Innovative Optical Health Sciences
2014, 7(1): 1450007
