Welcome to the InfoSci Platform
Reference Hub3
Quantum Local Binary Pattern for Medical Edge Detection

Quantum Local Binary Pattern for Medical Edge Detection

Somia Lekehali, Abdelouahab Moussaoui
Copyright: © 2019 |Volume: 12 |Issue: 2 |Pages: 17
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522564751|DOI: 10.4018/JITR.2019040103
Cite Article Cite Article

MLA

Lekehali, Somia, and Abdelouahab Moussaoui. "Quantum Local Binary Pattern for Medical Edge Detection." JITR vol.12, no.2 2019: pp.36-52. http://doi.org/10.4018/JITR.2019040103

APA

Lekehali, S. & Moussaoui, A. (2019). Quantum Local Binary Pattern for Medical Edge Detection. Journal of Information Technology Research (JITR), 12(2), 36-52. http://doi.org/10.4018/JITR.2019040103

Chicago

Lekehali, Somia, and Abdelouahab Moussaoui. "Quantum Local Binary Pattern for Medical Edge Detection," Journal of Information Technology Research (JITR) 12, no.2: 36-52. http://doi.org/10.4018/JITR.2019040103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Edge detection is one of the most important operations for extracting the different objects in medical images because it enables delimitation of the various structures present in the image. Most edge detection algorithms are based on the intensity variations in images. Edge detection is especially difficult when the images are textured, and it is essential to consider the texture in edge detection processes. In this article, the authors propose a new procedure to extract the texture from images, called the Quantum Local Binary Pattern (QuLBP). The authors introduce two applications that use QuLBP to detect edges in magnetic resonance images: a cellular automaton (CA) edge detector algorithm and a combination of the QuLBP and the Deriche-Canny algorithm for salt and pepper noise resistance. The proposed approach to extracting texture is designed for and applied to different gray scale image datasets with real and synthetic magnetic resonance imaging (MRI). The experiments demonstrate that the proposed approach produces good results in both applications, compared to classical algorithms.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.