Texture feature extraction using 2-D Gabor Filters

Texture feature extraction is a procedure of computing and describing the features and characteristics of image which numerically describes that texture image properties. This paper investigated texture feature extraction using 2-D Gabor Filter to extract the texture features of Inverse Fast Fourier...

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Published in2012 IEEE Symposium on Computer Applications and Industrial Electronics pp. 173 - 178
Main Authors Roslan, R., Jamil, N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
Brain
Computational modeling
Feature extraction
Fourier transforms
Frequency-domain analysis
Gabor filters
IFFT
Magnetic resonance imaging
MRI images
Texture Energy
Texture feature extraction
Transformation of IFFT
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ISBN1467330329
9781467330329
DOI10.1109/ISCAIE.2012.6482091

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Abstract Texture feature extraction is a procedure of computing and describing the features and characteristics of image which numerically describes that texture image properties. This paper investigated texture feature extraction using 2-D Gabor Filter to extract the texture features of Inverse Fast Fourier Transform (IFFT), texture energy and transformed IFFT. The Gabor filter bank experimented on seventy two collected samples of skull-stripped T1-weighted, T2-weighted and FLAIR MRI brain images utilizing four frequencies and four orientations. Results showed that texture feature extractions of two highest frequencies with all four orientations produced the highest acceptance rate.
AbstractList Texture feature extraction is a procedure of computing and describing the features and characteristics of image which numerically describes that texture image properties. This paper investigated texture feature extraction using 2-D Gabor Filter to extract the texture features of Inverse Fast Fourier Transform (IFFT), texture energy and transformed IFFT. The Gabor filter bank experimented on seventy two collected samples of skull-stripped T1-weighted, T2-weighted and FLAIR MRI brain images utilizing four frequencies and four orientations. Results showed that texture feature extractions of two highest frequencies with all four orientations produced the highest acceptance rate.
Author Roslan, R.
Jamil, N.
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  givenname: N.
  surname: Jamil
  fullname: Jamil, N.
  email: liza@tmsk.uitm.edu.my
  organization: Fac. of Comput. & Math. Sci., Univ. Teknol. MARA, Shah Alam, Malaysia
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Snippet Texture feature extraction is a procedure of computing and describing the features and characteristics of image which numerically describes that texture image...
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StartPage 173
SubjectTerms Brain
Computational modeling
Feature extraction
Fourier transforms
Frequency-domain analysis
Gabor filters
IFFT
Magnetic resonance imaging
MRI images
Texture Energy
Texture feature extraction
Transformation of IFFT
Title Texture feature extraction using 2-D Gabor Filters
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