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Weeds reduce cassava root yields and infest furrow areas quickly. The use of mechanical weeders has been introduced in Thailand; however, manually aligning the weeders with each planting row and at headland turns is still challenging. It is critical to clear weeds on furrow slopes and driveways via mechanical weeders. Automation can support this difficult work for weed management via driveway detection. In this context, deep learning algorithms have the potential to train models to detect driveways through furrow image segmentation. Therefore, the purpose of this research was to develop an image segmentation model for automated weed control operations in cassava plantation fields. To achieve this, image datasets were obtained from various fields to aid weed detection models in automated weed management. Three models—Mask R-CNN, YOLACT, and YOLOv8n-seg—were used to construct the image segmentation model, and they were evaluated according to their precision, recall, and FPS. The results show that YOLOv8n-seg achieved the highest accuracy and FPS (114.94 FPS); however, it experienced issues with frame segmentation during video testing. In contrast, YOLACT had no segmentation issues in the video tests (23.45 FPS), indicating its potential for driveway segmentation in cassava plantations. In summary, image segmentation for detecting driveways can improve weed management in cassava fields, and the further automation of low-cost mechanical weeders in tropical climates can be performed based on the YOLACT algorithm.

To quickly determine the blasting block degree and conduct an accurate and objective analysis of the tunnel blasting effect, this study has enhanced and improved upon the traditional genetic algorithm and Otsu algorithm. It has combined it with the marking watershed method and utilized ground digital acquisition to capture images of blasting debris. These images are then used in our custom-developed blasting analysis software to calculate the blasting block degree distribution and provide a quantitative analysis of blasting block degree. The research results show that the optimized image segmentation algorithm effectively improves the traditional threshold segmentation method on the poor effect of segmentation of the edge of the adherent block or the direct application of the watershed segmentation of the over-segmentation problem, to improve the segmentation accuracy based on the new segmentation technology is close to the traditional technology in terms of time. Through the self-developed software, the construction personnel in the project site to quickly obtain the blasting block degree histogram, block degree cumulative curve and other important indicators of the evaluation of the effect of blasting block degree, to provide data support for on-site construction, to assist in the modification of the blasting program, and to improve the efficiency of construction. This study realizes the rapid detection and block identification of blasting blocks, provides data support for the optimization of blasting parameters, and has good application and promotion value.

To address the current reliance on manual labor in traditional logistics sorting operations, which leads to low sorting efficiency and high operational costs, this study presents the design of an unmanned logistics vehicle based on the Robot Operating System (ROS). To overcome bounding-box loss issues commonly encountered by mainstream video-stream image segmentation algorithms under complex conditions, the novel SMITE video image segmentation algorithm is employed to accurately extract key regions of mail items while eliminating interference. Extracted logistics information is mapped to corresponding grid points within a map constructed using Simultaneous Localization and Mapping (SLAM). The system performs global path planning with the A* heuristic graph search algorithm to determine the optimal route, autonomously navigates to the target location, and completes the sorting task via a robotic arm, while local path planning is managed using the Dijkstra algorithm. Experimental results demonstrate that the SMITE video image segmentation algorithm maintains stable and accurate segmentation under complex conditions, including object appearance variations, illumination changes, and viewpoint shifts. The PaddleOCR text recognition algorithm achieves an average recognition accuracy exceeding 98.5%, significantly outperforming traditional methods. Through the analysis of existing technologies and the design of a novel parcel-grasping control system, the feasibility of the proposed system is validated in real-world environments.

Image segmentation algorithms are increasingly used in the research direction of remote sensing soil and water conservation measures. In this paper, the basic concepts and characteristic parameters of image segmentation algorithm are elaborated, and Songpan County is taken as the study area, and the remote sensing data obtained in the study area are corrected and preprocessed to reduce the error in order to get the image as close as possible to the real scene. Apply the multi-scale segmentation technology to segment the remote sensing image, and elaborate the principle and process of multi-scale segmentation, divide the research scale of this paper into four levels, 50, 80, 120, 170, through the area ratio mean method, and set the appropriate segmentation parameters based on these four levels. By analyzing and evaluating the classification accuracy of the classification thematic map, it is clearly known that the 10m raster size is the size that is easy to reduce the accuracy degree of land type, especially in the classification of grassland and forest land, which is easy to be misclassified and omitted, and it needs the researchers to pay more attention to it. The study of information extraction and protection methods of soil and water conservation measures in remote sensing images based on image segmentation algorithm can provide corresponding support for improving the quality of soil and water conservation in China.

Fuzzy C-means (FCM) is one of the prominent and effective cluster-based image segmentation techniques exceedingly susceptible to noise and initial cluster centers, thereby effortlessly converging toward local optima. However, FCM consumes exceptionally high computation time due to the repetitive computation of the distance amid cluster centers and pixels. To resolve this apprehension, this paper aims to consider a histogram-based fast fuzzy image clustering (HBFFIC) procedure that primarily tends to carry out morphological reconstruction (MR) operation over the image to assure noise immunity and safeguard details of the imagery. Further, as a replacement for pixels of a summed image, clustering is carried out based on gray-level histogram. This with no qualm radically trims down the computational time as the number of gray levels in an image is normally to a great extent lesser than that of the number of its pixels. Though HBFFIC is a proficient local optimizer however, owing to arbitrary initialization that is carried out in FCM, HBFFIC has the utmost possibility to get effortlessly wedge into local optima. Consequently, this is where the role of nature-inspired optimization algorithms (NIOA) comes into picture. For that reason, this paper successfully makes use of NIOA to prevail over the dilemma using Archimedes optimizer (AO) to discover the most favorable cluster centers. The real-world images particularly synthetic, grayscale, and color pathology images are exercised to perform the experimental study. The experimental study clearly highlights that the proposed hybrid algorithm (HBFFIC-AO) for noisy image segmentation outperforms the other state-of-art algorithms in terms of segmentation accuracy (SA), comparison score (CS), MSE, and PSNR. The visual along with numerical outcomes projected in the experimental study point toward the pre-eminence of the proposed algorithm as compared with the prevailing leading-edge image segmentation algorithms.

Changes in superficial cerebral veins (SCV) caused by different cognitive levels were observed using MR susceptibility-weighted imaging (MR-SWI) to explore the vascular mechanism underlying human brain aging and potential biomarkers of cognitive decline . Three hundred and sixty-four participants (184 males,180 females and aged 18-79 years) were included in this study. The quantitative features of SCVs in the cerebral hemispheres were collected via MR-SWI and were processed with an artificial intelligence (AI) image segmentation algorithm. The changes in the morphology and structure of the SCVs were analyzed with SPSS software. The quantitative value of SCV were significantly greater in males than in females. In higher age groups, the total number of SCVs and the number of SCVs in the left and right cerebral hemispheres significantly decreased. The number of SCVs in hypertensive patients was significantly lower than that in non-hypertensive patients. Additionally, the diameter, curvature and length of SCVs in the right cerebral hemispheres were significantly lower in anemic patients than in non-anemic patients. The number and length of SCVs in the bilateral cerebral hemispheres were negatively correlated with the rate of cognitive abnormalities. Among tea drinkers in the youth group, the number of SCVs in both hemispheres were negatively correlated with total tau protein (T-tau), and the curvature of SCVs in the right hemisphere was negatively correlated with phospho-tau181(P-tau181) and T-tau concentrations in venous blood. There was a negative correlation between the T-tau concentration in venous blood and tea consumption. The curvature of SCVs in the right cerebral hemisphere had a significant impact on cognitive decline, with a strong positive correlation. The length of SCV in the right hemisphere of the brain had a significant negative correlation with cognitive decline, however, this correlation was relatively weak. The quantitative value of SCV was negatively correlated with cognitive decline. Daily tea consumption may have a positive impact on the quantitative features of SCVs in the young group. As SCVs are a component of the glymphatic system, their blood flow may affect the clearance of toxic proteins.

Fuzzy clustering algorithm is the main method of image segmentation, but it can’t be widely used in various fields. Therefore, an image segmentation algorithm based on improved fuzzy clustering was proposed in this paper. The fuzzy clustering theory and analysis method were described in detail, and the research of image segmentation algorithm was discussed in this paper. And then the method of sub graph decomposition and region merging was used to improve the clustering method of fuzzy C mean clustering image segmentation algorithm and the algorithm was verified by an example. The results showed that the algorithm was feasible. Compared with other existing algorithms, the algorithm had more advantages in running time and image segmentation accuracy.

Objective: Aiming at the problem of low accuracy in extracting small blood vessels from existing retinal blood vessel images, a retinal blood vessel segmentation method based on a combination of a multi-scale linear detector and local and global enhancement is proposed. Methods: The multi-scale line detector is studied, and it is divided into two parts: small scale and large scale. The small scale is used to detect the locally enhanced image and the large scale is used to detect the globally enhanced image. Fusion the response functions at different scales to get the final retinal vascular structure. Results: Experiments on two databases STARE and DRIVE, show that the average vascular accuracy rates obtained by the algorithm reach 96.62% and 96.45%, and the average true positive rates reach 75.52% and 83.07%, respectively. Conclusion: The segmentation accuracy is high, and better blood vessel segmentation results can be obtained. doi: https://doi.org/10.12669/pjms.37.6-WIT.4848 How to cite this:Hao Y, Xie H, Qiu R. Construction and application of color fundus image segmentation algorithm based on Multi-Scale local combined global enhancement. Pak J Med Sci. 2021;37(6):1595-1599. doi: https://doi.org/10.12669/pjms.37.6-WIT.4848 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Purpose To observe the regulation of cerebral circulation in vivo based on image segmentation algorithms for deep learning in medical imaging to automatically detect and quantify the neonatal deep medullary veins (DMVs) on susceptibility weighted imaging (SWI) images. To evaluate early cerebral circulation self-rescue for neonates undergoing risk of cerebral hypoxia-ischaemia in vivo. Methods SWI images and clinical data of 317 neonates with or without risk of cerebral hypoxia-ischaemia were analyzed. Quantitative parameters showing the number, width, and curvature of DMVs were obtained using an image segmentation algorithm. Results The number of DMVs was greater in males than in females ( p  < 0.01), and in term than in preterm infants ( p  = 0.001). The width of DMVs was greater in term than in preterm infants ( p  < 0.01), in low-risk than in high-risk group ( p  < 0.01), and in neonates without intracranial extracerebral haemorrhage (ICECH) than with ICECH ( p  < 0.05). The curvature of DMVs was greater in term than in preterm infants ( P  < 0.05). The width of both bilateral thalamic veins and anterior caudate nucleus veins were positively correlated with the number of DMVs; the width of bilateral thalamic veins was positively correlated with the width of DMVs. Conclusion The DMVs quantification based on image segmentation algorithm may provide more detailed and stable quantitative information in neonate. SWI vein quantification may be an observable indicator for in vivo assessment of cerebral circulation self-regulation in neonatal hypoxic-ischemic brain injury.

Objective: We used U-shaped convolutional neural network (U_Net) multi-constraint image segmentation method to compare the diagnosis and imaging characteristics of tuberculosis and tuberculosis with lung cancer patients with Computed Tomography (CT). Methods: We selected 160 patients with tuberculosis from the severity scoring (SVR) task is provided by Image CLEF Tuberculosis 2019. According to the type of diagnosed disease, they were divided into tuberculosis combined with lung cancer group and others group, all patients were given chest CT scan, and the clinical manifestations, CT characteristics, and initial suspected diagnosis and missed diagnosis of different tumor diameters were observed and compared between the two groups. Results: There were more patients with hemoptysis and hoarseness in pulmonary tuberculosis combined with lung cancer group than in the pulmonary others group (P<0.05), and the other symptoms were not significantly different (P>0.05). Tuberculosis combined with lung cancer group had fewer signs of calcification, streak shadow, speckle shadow, and cavitation than others group; however, tuberculosis combined with lung cancer group had more patients with mass shadow, lobular sign, spines sign, burr sign and vacuole sign than others group. Conclusion: The symptoms of hemoptysis and hoarseness in pulmonary tuberculosis patients need to consider whether the disease has progressed and the possibility of lung cancer lesions. CT imaging of pulmonary tuberculosis patients with lung cancer usually shows mass shadows, lobular signs, spines signs, burr signs, and vacuoles signs. It can be used as the basis for its diagnosis. Simultaneously, the U-Net-based segmentation method can effectively segment the lung parenchymal region, and the algorithm is better than traditional algorithms. doi: https://doi.org/10.12669/pjms.37.6-WIT.4795 How to cite this:Zhu F, Zhang B. Analysis of the Clinical Characteristics of Tuberculosis Patients based on Multi-Constrained Computed Tomography (CT) Image Segmentation Algorithm. Pak J Med Sci. 2021;37(6):1705-1709. doi: https://doi.org/10.12669/pjms.37.6-WIT.4795 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.