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All Journal International Journal of Electrical and Computer Engineering
Dac-Nhuong Le
Haiphong University
Computer Science & IT Electrical & Electronics Engineering

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An effective RGB color selection for complex 3D object structure in scene graph systems Chung Le Van; Gia Nhu Nguyen; Tri Huu Nguyen; Tung Sanh Nguyen; Dac-Nhuong Le
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2047.254 KB) | DOI: 10.11591/ijece.v10i6.pp5951-5964

Abstract

The goal of this project is to develop a complete, fully detailed 3D interactive model of the human body and systems in the human body, and allow the user to interacts in 3D with all the elements of that system, to teach students about human anatomy. Some organs, which contain a lot of details about a particular anatomy, need to be accurately and fully described in minute detail, such as the brain, lungs, liver and heart. These organs are need have all the detailed descriptions of the medical information needed to learn how to do surgery on them, and should allow the user to add careful and precise marking to indicate the operative landmarks on the surgery location. Adding so many different items of information is challenging when the area to which the information needs to be attached is very detailed and overlaps with all kinds of other medical information related to the area. Existing methods to tag areas was not allowing us sufficient locations to attach the information to. Our solution combines a variety of tagging methods, which use the marking method by selecting the RGB color area that is drawn in the texture, on the complex 3D object structure. Then, it relies on those RGB color codes to tag IDs and create relational tables that store the related information about the specific areas of the anatomy. With this method of marking, it is possible to use the entire set of color values (R, G, B) to identify a set of anatomic regions, and this also makes it possible to define multiple overlapping regions.
An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding Sirshendu Hore; Souvik Chakraborty; Sankhadeep Chatterjee; Nilanjan Dey; Amira S. Ashour; Le Van Chung; Dac-Nhuong Le
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (566.449 KB) | DOI: 10.11591/ijece.v6i6.pp2773-2780

Abstract

Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.
Co-Authors Amira S. Ashour Chung Le Van Gia Nhu Nguyen Le Van Chung Nilanjan Dey Sankhadeep Chatterjee Sirshendu Hore Souvik Chakraborty Tri Huu Nguyen Tung Sanh Nguyen