Functional measurement of a supplementary teaching system based on augmented reality technology for the course “building mechanical services and utilities” in architecture

Background and Objective:The advancement of information technology in the field of portable technologies has made it possible to develop omnipresent learning. Mobile learning (learning everywhere) is a new learning environment in which the learner is placed in a real-world scenario, with access to online resources, through portable tools and wireless networks. On the other hand, augmented reality has helped to complement human sensory perceptions of the environment by positioning them in the middle of the real world and the virtual world and creating an environment in which virtual components are combined in a dynamic interaction with the real environment. Portable augmented reality technology is a great tool for adding content to field visits by adding virtual components and information to a specific physical location. Such a tool can change the student-centered and inactive educational process into a student-centered and active process by creating a self-sufficient learning situation for students. The learning environment resulting from the combination of the real world and the virtual world is effective in creating a valid learning environment for students. Numerous studies have examined the application of augmented reality technologies in various educational fields such as engineering, medicine, ecology, science, art, history, etc. This study has used a tool based on augmented reality technology to enhance the efficiency of regular visits in teaching technical courses in the field of architecture. Methods: This study is applied utilizing a quantitative research method.  Participants included 73 students in the mechanical engineering course divided into experimental groups (38) and control group (35) after an initial theoretical training and administering pre-tests. The instruments in this study were tests and questionnaires. The experiment took place over a three-week period creating an active learning environment. Findings: The results of the study show that the application of the AR supplementary teaching tool contributes to enhance the students’ learning through the field visits and it is more effective than field visits in order to provide the satisfaction of learning approach and higher scientific validity from the students’ point of view. Conclusion: The use of AR technology and the focus on important points in field visits have made the teaching and learning process more efficient and enjoyable for students. From the students' point of view, the knowledge credibility of the activity designed for the experimental group was higher than the activity designed for the control group. The combination of building information in a simple and understandable software caused valid and superior knowledge.   ===================================================================================== COPYRIGHTS  ©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

Density-Based Histogram Partitioning and Local Equalization for Contrast Enhancement of Images

Histogram Equalization technique is one of the basic methods in image contrast enhancement. Using this method, in the case of images with uniform gray levels (with narrow histogram), causes loss of image detail and the natural look of the image. To overcome this problem and to have a better image contrast enhancement, a new two-step method was proposed. In the first step, the image histogram is partitioned into some sub-histograms according to mean value and standard deviation, which will be controlled with PSNR measure. In the second step, each sub-histogram will be improved separately and locally with traditional histogram equalization. Finally, all sub-histograms will be combined to obtain the enhanced image. Experimental results shows that this method would not only keep the visual details of the histogram, but also enhance image contrast

Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

<p>Abstract</p> <p>Background</p> <p>In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images.</p> <p>This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain.</p> <p>Methods</p> <p>The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system.</p> <p>Results</p> <p>The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain.</p> <p>Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related.</p> <p>Conclusions</p> <p>Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use.</p

Computer-assisted approaches for uterine fibroid segmentation in MRgFUS treatments: Quantitative evaluation and clinical feasibility analysis

Nowadays, uterine fibroids can be treated using Magnetic Resonance guided Focused Ultrasound Surgery (MRgFUS), which is a non-invasive therapy exploiting thermal ablation. In order to measure the Non-Perfused Volume (NPV) for treatment response assessment, the ablated fibroid areas (i.e., Region of Treatment, ROT) are manually contoured by a radiologist. The current operator-dependent methodology could affect the subsequent follow-up phases, due to the lack of result repeatability. In addition, this fully manual procedure is time-consuming, considerably increasing execution times. These critical issues can be addressed only by means of accurate and efficient automated Pattern Recognition approaches. In this contribution, we evaluate two computer-assisted segmentation methods, which we have already developed and validated, for uterine fibroid segmentation in MRgFUS treatments. A quantitative comparison on segmentation accuracy, in terms of area-based and distance-based metrics, was performed. The clinical feasibility of these approaches was assessed from physicians’ perspective, by proposing an integrated solution