Abstract
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%.
Highlights
Bombyx mori particulate disease is one of the five major infectious diseases of Bombyx mori.It causes devastating harm to Bombyx mori and is listed as a quarantine object by silkwormbreeding countries around the world [1]
Analysis order to verify theand effectiveness of the color feature-based micro-particle microscopic image region-growing segmentation algorithm proposed in this microscopic paper, this experIn order to verify the effectiveness of the color feature-based micro-particle image region-growing segmentation algorithm proposed in this paper, this experiment was on eniment was divided into two parts: (1) micro-particle color pre-segmentation based divided into two parts: (1)
Micro-particle color pre-segmentation based on enhanced color, hanced color, and (2) combined with gray information, the pre-segmented image is grown and (2) combined with gray information, the pre-segmented image is grown in regions
Summary
Bombyx mori particulate disease is one of the five major infectious diseases of Bombyx mori. Mandyartha et al [5] set multi-level automatic thresholds to segment the blood cell images Through this method, the average Zijdenbos similarity index (ZSI) and recall accuracy reached 92.5% and 94.03%, respectively. Pan [7] used a custom convolution kernel for edge extraction of images in cancer cell diagnosis and an enhanced edge to extract cancer cells combined with the tophat method, reaching a result of a higher edge closure of 30%, at least compared with other edge detection algorithms (e.g., Roberts, Log, Canny and Prewitt) These methods require relatively high contrast cell images, and the edge segmentation is poor in low-contrast cell microscopic images.
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