This article presents the application of the concept of usability in virtual learning environment which implement teaching and learning strategies, using information and communication technologies (ICT), just as students have the necessary environment where they can obtain, use or share various interactive teaching materials developed and created using exelearning, and exported to SCORM (Sharable Content Object Reference Model / Object Reference Model Shareable Content), allowing more easily raise the educational package to IG (Integrated Grid) virtual classrooms. The concept of usability is applied to allows virtual environment determine the degree of satisfaction generated by the end user application.
Conflicto de intereses: Los autores declaran no tener ningún interés financiero relacionado con el contenido de este artículo. Financiación: No hubo fuentes externas de financiación para este trabajo.
The multi-slice computerized tomography (MSCT) is a medical Background: imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention.This approach consists of three stages. The initial stage, an image Methods: enhancement, consists of a method for correcting non homogeneities present in the background of MSCT images. Then, a segmentation stage using a clustering method allows to obtain the adenocarcinoma morphology. In the third stage, the pathology region is reconstructed and then visualized with a three-dimensional (3-D) computer graphics procedure based on marching cubes algorithm. In order to validate the segmentations, the Dice score is used as a metric function useful for comparing the segmentations obtained using the proposed method with respect to ground truth volumes traced by a clinician.A total of 8 datasets available for patients diagnosed, from the cancer Results: data collection of the project, Cancer Genome Atlas Stomach Adenocarcinoma (TCGASTAD) is considered in this research. The volume of the type 2 stomach tumor is estimated from the 3-D shape computationally segmented from the each dataset. These 3-D shapes are computationally reconstructed and then used to assess the morphopathology macroscopic features of this cancer.The segmentations obtained are useful for assessing Conclusions:qualitatively and quantitatively the stomach type 2 cancer. In addition, this type
Pattern recognition is a relevant research area in artificial vision, where several methods have been proposed in the last 50 years. This paper presents a real-time pattern recognition algorithm for an addition operation through two six-sided dice using an Android camera device, an IP webcam app, a graphical user interface (GUIs) from Matlab, and Arduino technology. The methodology to develop the interface and the communication between Matlab software and Arduino technology is presented. To evaluate the performance of the proposed methodology, a real-time implementation using an Arduino Mega 2560 board and Matlab is illustrated.
Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three stages. The initial stage, an image enhancement, consists of a method for correcting non homogeneities present in the background of MSCT images. Then, a segmentation stage using a clustering method allows to obtain the adenocarcinoma morphology. In the third stage, the pathology region is reconstructed and then visualized with a three–dimensional (3–D) computer graphics procedure based on marching cubes algorithm. In order to validate the segmentations, the Dice score is used as a metric function useful for comparing the segmentations obtained using the proposed method with respect to ground truth volumes traced by a clinician. Results: A total of 8 datasets available for patients diagnosed, from the cancer data collection of the project, Cancer Genome Atlas Stomach Adenocarcinoma (TCGASTAD) is considered in this research. The volume of the type 2 stomach tumor is estimated from the 3–D shape computationally segmented from the each dataset. These 3–D shapes are computationally reconstructed and then used to assess the morphopathology macroscopic features of this cancer. Conclusions: The segmentations obtained are useful for assessing qualitatively and quantitatively the stomach type 2 cancer. In addition, this type of segmentation allows the development of computational models that allow the planning of virtual surgical processes related to type 2 cancer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.