Considering the effect of using JPEG images on accuracy results of radiology images and application programs
Mirzaei H.1, Jafari M.2, Mirshahi A.3
1Mirzaei H., Department of Computer, Science and Research branch, Islamic Azad University, Kerman, Iran.
2Dr. Jafari M., Department of Computer, Science and Research branch, Islamic Azad university, Kerman, Iran.
3Dr. Mirshahi A., Department of Clinical Sciences, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad (FUM), Mashhad, Iran.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 489-492 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1159112612/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With hardware and software algorithms development, necessity of methods with accuracy and velocity are more attention. For example edge detection is one of the most important operations in machine vision and the main purpose of edge detection is reducing the volume of data with preserving main structure and original form of images. For these purpose the accuracy of edge detection with retrieval edges by minimum position error rate and losing edges is one of more important approach in recent decades. It is clear those raw images because of no losing many features than JPEG images have better results. In this research consider a simulation neural network program and compare three famous edge detection “Sobel, Prewitt, Canny” with raw images and shows efficiency on results.
Keywords: Medical image processing, Edge detection, Raw image, Neural network.