Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features
Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features
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DOI：
10.3390/sym13122325
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发表时间：
2021-12-01
期刊：
影响因子：
2.7
通讯作者：
Ye, Jun
中科院分区：
文献类型：
--
作者：
Hu, Xinyu;Chen, Qi;Ye, Jun
蚕微粒病是世界各国检测蚕病的法定检疫标准。目前常用的检测方法——巴斯德手动显微镜法，在全世界范围内检测效率较低。目前巴斯德手动显微镜检测方法效率低下，使得应用机器视觉技术检测微粒孢子成为推进蚕病研究的重要技术。针对检测方案中采集的蚕微粒孢子椭球对称形状显微图像对比度低、光照条件不同、图像背景复杂的问题，提出一种基于微粒颜色和灰度信息的区域生长分割方法。该方法利用模糊对比度增强算法来增强微粒子的颜色信息，提高微粒子与背景的区分度。在颜色稳定的HSV颜色空间中，提取微粒子的颜色信息作为种子点，消除光线的影响，减少杂质的干扰，从而准确定位微粒子的分布区域。结合邻域伽玛变换，增强灰度图像中微粒目标的高亮特征，进行区域生长。同时，从复杂背景中分割出准确、完整的微粒子目标，减少了复杂背景中单一特征造成的背景杂质分割。为了评估分割性能，我们计算了该方法分割的微粒样本图像与其对应的真值图像的IOU，实验表明，利用区域生长技术结合颜色和灰度特征，可以准确、完整地分割复杂背景下的微粒目标，分割精度IOU高达83.1%。
Silkworm microparticle disease is a legal quarantine standard in the detection of silkworm disease all over the world. The current common detection method, the Pasteur manual microscopy method, has a low detection efficiency all over the world. The low efficiency of the current Pasteur manual microscopy detection method makes the application of machine vision technology to detect microparticle spores an important technology to advance silkworm disease research. For the problems of the low contrast, different illumination conditions and complex image background of microscopic images of the ellipsoidal symmetrical shape of silkworm microparticle spores collected in the detection solution, a region growth segmentation method based on microparticle color and grayscale information is proposed. In this method, the fuzzy contrast enhancement algorithm is used to enhance the color information of micro-particles and improve the discrimination between the micro-particles and background. In the HSV color space with stable color, the color information of micro-particles is extracted as seed points to eliminate the influence of light and reduce the interference of impurities to locate the distribution area of micro-particles accurately. Combined with the neighborhood gamma transformation, the highlight feature of the micro-particle target in the grayscale image is enhanced for region growing. Mea6nwhile, the accurate and complete micro-particle target is segmented from the complex background, which reduces the background impurity segmentation caused by a single feature in the complex background. In order to evaluate the segmentation performance, we calculate the IOU of the microparticle sample image segmented by this method with its corresponding true value image, and the experiments show that the combination of color and grayscale features using the region growth technique can accurately and completely segment the microparticle target in complex backgrounds with a segmentation accuracy IOU as high as 83.1%.