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Edge detection using Chebyshev's orthogonal polynomial and brain extraction from magnetic resonance images of human head
Author(s) -
Somasundaram K.,
Kalaividya P. A.,
Kalaiselvi T.,
Krishnamoorthy R.,
Praveenkumar S.
Publication year - 2019
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22297
Subject(s) - jaccard index , edge detection , computer science , canny edge detector , segmentation , similarity (geometry) , algorithm , detector , chebyshev polynomials , artificial intelligence , prewitt operator , polynomial , computer vision , pattern recognition (psychology) , mathematics , image processing , image (mathematics) , mathematical analysis , telecommunications
In this article, we propose a new edge detecting method based on the transform coefficients obtained by a point spread function constructed out of Chebyshev's orthogonal polynomials. This edge detector finds edges similar to that of Prewitt and Roberts but is robust against additive and multiplicative noises. We also propose a new scheme to extract brain portion from the magnetic resonance images (MRI) of human head scan by making use of the of the new edge detector. The proposed scheme involves edge detection, morphological operations, and largest connected component analysis. Experiments conducted by applying the proposed scheme on 19 volumes of MRI collected from Internet Brain Segmentation Repository (IBSR) show that the proposed brain extraction scheme performed better than the popular Brain Extraction Tool (BET). The performance of the proposed scheme is measured by computing the Dice coefficient (D) and Jaccard similarity index (J). The proposed method produced a value of 0.9068 for D and 0.8321 for J.