Research on image segmentation algorithm based on threshold

Song, Shaozhong; Gao, Ting

doi:10.1109/icmtma52658.2021.00071

Cited by 8 publications

(3 citation statements)

References 1 publication

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“…Since the degree of mechanical damage of mandarin largely affects the decay rate during the subsequent storage period, it is necessary to explore the relationship between mandarin damage characteristics and storage decay rate. As a classical image processing method, image segmentation has a good effect on extracting local features of images and is an important means for further analysis after obtaining images [ 22 ]. In this paper, an image segmentation process and method for specific mandarin sectional images was proposed (written in results and analysis).…”

Section: Methodsmentioning

confidence: 99%

Mechanical Damage Caused by Compression and Its Effects on Storage Quality of Mandarin

Tian,

Chen,

2024

Foods

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Mandarin is vulnerable to a range of external loads during processing and shipping, which can cause interior mechanical damage that can happen right away or over time and cause serious rotting when kept in storage. In this study, mandarin was treated to a certain quantity of compression load that did not result in a noticeable rupture of the peel. The interior pulp structure of mandarin was examined for damage prior to peel damage using CT scanning and image reconstruction. An image segmentation method based on mask processing was then used to calculate the pulp damage rate. We examined the variations in physiological activities and internal components between the test group that underwent compression load and the control group that did not undergo this type of stress during storage. The aim was to investigate the factors that contributed to the faster decay of mandarin following mechanical damage. Regression analysis was also used to establish a quantifiable relationship between the amount of compression deformation and the rates of damage and decay of mandarin during storage. The findings demonstrated that mandarin pulp exhibited visible mechanical damage when compression deformation exceeded 8 mm. This led to the disruption of physiological processes like respiration and polysaccharide breakdown, which in turn decreased the hardness of the fruit and sped up its rotting. This study identifies the critical range of compression deformation that leads to the beginning of pulp damage in mandarins. Additionally, it clarifies the quality deterioration mechanism of mandarins that have been subjected to compression damage during the storage period. Therefore, in practical production, various methods of picking, sorting, and collecting mandarins can be optimized to control the amount of compression deformation within a suitable range. This will reduce the probability of pulp damage. According to the study’s conclusions, storage conditions can be optimized to regulate the physiological activities of mandarins in a targeted manner. This can minimize the probability of fruit decay and reduce economic losses.

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Section: Methodsmentioning

confidence: 99%

Mechanical Damage Caused by Compression and Its Effects on Storage Quality of Mandarin

Tian,

Chen,

2024

Foods

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“…Full-scale skip join contains high-level semantics with low-level details in feature maps from different scales, and depth monitoring learns the hierarchical representation from the full-scale aggregation feature map. Song et al [ 52 ] compared the threshold-based segmentation methods and found that the Ostu method was used to divide the picture into many small blocks, and then determined the local threshold of each small piece, which can be well segmented in the case of uneven illumination and blurred image. Gao et al [ 53 ] proposed an image segmentation strategy based on band conversion, making detailed segmentation in horizontal, vertical, and diagonal by using wavelet transform theory.…”

Section: Related Workmentioning

confidence: 99%

AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma

Tang

Huang

et al. 2022

Healthcare

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Osteosarcoma is a malignant tumor derived from primitive osteogenic mesenchymal cells, which is extremely harmful to the human body and has a high mortality rate. Early diagnosis and treatment of this disease is necessary to improve the survival rate of patients, and MRI is an effective tool for detecting osteosarcoma. However, due to the complex structure and variable location of osteosarcoma, cancer cells are highly heterogeneous and prone to aggregation and overlap, making it easy for doctors to inaccurately predict the area of the lesion. In addition, in developing countries lacking professional medical systems, doctors need to examine mass of osteosarcoma MRI images of patients, which is time-consuming and inefficient, and may result in misjudgment and omission. For the sake of reducing labor cost and improve detection efficiency, this paper proposes an Attention Condenser-based MRI image segmentation system for osteosarcoma (OMSAS), which can help physicians quickly locate the lesion area and achieve accurate segmentation of the osteosarcoma tumor region. Using the idea of AttendSeg, we constructed an Attention Condenser-based residual structure network (ACRNet), which greatly reduces the complexity of the structure and enables smaller hardware requirements while ensuring the accuracy of image segmentation. The model was tested on more than 4000 samples from two hospitals in China. The experimental results demonstrate that our model has higher efficiency, higher accuracy and lighter structure for osteosarcoma MRI image segmentation compared to other existing models.

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“…Image segmentation is a difficult problem in understanding image analysis and computer vision [2]. In image processing, the segmentation method is used to separate objects from the image background [3], [4]. Image segmentation is widely used in the medical world, including to identify organ pixels or lesions from medical images such as CT-scan or magnetic resonance imaging (MRI) [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Image Transformation With Lung Image Thresholding and Segmentation Method

Sitanggang¹,

Yuhandri

Setiawan³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Image transformation is important to obtain and find certain information about an image that was not previously known, such as pixels, geometry, size, and color. Following this, this research aims to analyze image transformation in producing better values using threshold and segmentation methods. The segmentation process is carried out based on two color models, namely hue saturation value (HSV) and red green blue (RGB). The image data used in this study was the x-ray image of the lungs from www.fk.unair.ac.id. which is processed using the Matlab 2021a application to help the analysis process. on the results of the image segmentation analysis carried out in this case, the greater the HSV and RGB threshold values used in the image data, the better and clearer the segmentation of the detected image results. In other words, the size of the thresholding value generated greatly affects the quality, brightness, size, and color of the resulting image. The best lung X-ray image segmentation results were obtained when using the threshold values HSV = 0.9 and RGB = 9.

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Research on image segmentation algorithm based on threshold

Cited by 8 publications

References 1 publication

Mechanical Damage Caused by Compression and Its Effects on Storage Quality of Mandarin

Mechanical Damage Caused by Compression and Its Effects on Storage Quality of Mandarin

AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma

Image Transformation With Lung Image Thresholding and Segmentation Method

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