The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an ...
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The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an automatic tumor detection algorithm to assist radiologists and physicians for brain tumor diagnosis. The aim of the present study is to automatically detect the location of the tumor in a brain MRI image with high accuracy. For this end, in the proposed algorithm, first, the skull is separated from the brain using morphological operators. The image is then segmented by six evolutionary algorithms, i.e., Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic algorithm (GA), Differential Evolution (DE), Harmony Search (HS), and Gray Wolf Optimization (GWO), as well as two other frequently-used techniques in the literature, i.e., K-means and otsu thresholding algorithms. Afterwards, the tumor area is isolated from the brain using the four features extracted from the main tumor. Evaluation of the segmented area revealed that the PSO has the best performance compared with the other approaches. The segmented results of the PSO are then used as the initial curve for the Active contour to precisely specify the tumor boundaries. The proposed algorithm is applied on fifty images with two different types of tumors. Experimental results on T1-weighted brain MRI images show a better performance of the proposed algorithm compared to other evolutionary algorithms, K-means, and otsuthresholding methods.
Segmentation from document images with a complex background is presented. The background component is estimated by low-rank modelling. With the estimated background component, the original image is further transformed...
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Segmentation from document images with a complex background is presented. The background component is estimated by low-rank modelling. With the estimated background component, the original image is further transformed into a compensated image which is easily segmented with otsu's thresholdingalgorithm to obtain a good binarisation result. Experiments on the Bickley diary and the DIBCO 2013 datasets validate the feasibility of the proposed method.
To segment defects from the quad flat non-lead QFN package surface a multilevel otsuthresholding method based on the firefly algorithm with opposition-learning is proposed. First the otsu thresholding algorithm is ex...
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To segment defects from the quad flat non-lead QFN package surface a multilevel otsuthresholding method based on the firefly algorithm with opposition-learning is proposed. First the otsu thresholding algorithm is expanded to a multilevel otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is *** the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel otsuthresholding method based on particle swarm optimization and the multilevel otsuthresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.
Nowadays, as modern cryptocurrency machines operate faster and hotter, the fans are insufficient to cool constant CPU and GPU usage. The researchers built specialized Mining Rig Cases for these machines to withstand e...
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ISBN:
(纸本)9781665483797
Nowadays, as modern cryptocurrency machines operate faster and hotter, the fans are insufficient to cool constant CPU and GPU usage. The researchers built specialized Mining Rig Cases for these machines to withstand excessive heat. Aside from temperature, another environmental factor that affects the performance of Cryptocurrency machines is relative humidity. The researchers created an IoT-based Monitoring system to regulate and monitor the Cryptocurrency machines as excessive heat and humidity can decrease the lifespan of these machines. The Infrared camera used color mapping and otsu's Segmentation algorithm for image processing. otsu Segmentation algorithm separates the background and foreground of the image while Color Mapping algorithm converts colors of the image into temperature. The researchers also tested the power consumption and data sizes to determine the efficiency of the monitoring system. Using Convolutional Neural Network, the researchers trained 300 images to assess the state of the Cryptocurrency Mining rig. Additionally, a two-tailed T-Test will determine any significant difference between the two algorithms. Upon training the images, the results obtained show that the two-tail P-value of temperature and humidity is 0.35 and 0.2566, respectively, which affirms no significant difference. However, the power consumption and data size had a substantial difference with P-values of 0.0004 and 3E-06, respectively. Moreover, it shows that the application of otsu reduces the data size and consumes more power due to deep learning.
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