Nazori Agani


Texture is an important characteristic that can be used for identification and detection for surface defect or abnormalities. This research has an algorithm for identifying heart with suspected myocardial infarction problem based on texture analysis applied on echocardiography images. Texture tissue sample images taken from echocardiography sub-image (ROI).  There are two tissue classes: Type 1 corresponds to normal myocardial tissue, whereas Type 2 corresponds to infarcted myocardium with small dimension. Therefore, in order to investigate possible in differences tissue between patient with infarction tissue or not, we proposed a Wavelet Extension Transform and Gray Level Co-occurrence matrix.

Wavelet Extension Transform is used to form an image approximation with higher resolution. The gray level co-occurrence matrices are computed for each sub-band. The feature vector of testing image and other feature vector as normal image classified by Mahalanobis distance to decide whether the test image is infarction or not. The method is tested with real data from echocardiography images of human heart. For each patient to be analyzed tissue samples are  taken from not-affected area  and tissue samples are taken from image segments corresponding to the infarcted area of myocardium. The result of this experiment can detect difference image from echocardiography as normal myocardium and infarcted myocardial tissue.

Full Text:

PDF (Indonesian)


. S.G.Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation.” IEEE Trans. on Pattern Analysis and Machine Inteligence, vol. 11, no. 7, pp.674-693, July 1989.

. S.G.Mallat, “Multifrequency Channel Decomposition of Images and Wavelet Models.” IEEE Transaction on Acoustics, Speech and Signal Processing, Vol.37, No.12, December 1989.

. I.Daubechies, “The Wavelet Transform, Time-Frequency Localization and Signal Analysis.” IEEE Trans. on Information Theory, vol. 36, no.5, pp.961-1004, September 1990.

. A.Materka and M.Strzelecki, “Tecture Analysis Methods - A Review,” Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels 1998.

. M.Antonini, M.Barlaud, P.Mathieu, and I.Daubechies, “Image Coding Using Wavelet Transform.” IEEE Trans. on Image Processing, vol. 1, no. 2, pp.205-220, April 1992.

. T.Chang, and C.C.Jay Kuo, “Texture Analysis and Classification with Tree-Structured Wavelet Transform.” IEEE Trans. on Image Processing, vol. 2, no. 4, pp.429-441, October 1993.

. D.Dunn, and W.E. Higgins, “Optimal Gabor Filters for Texture Segmentation.” IEEE Trans. on Image Processing, vol. 4, no. 7, pp.947-964, July 1995.

. Amara Graps, “An Introduction to Wavelet,” IEEE Computational Sciene and Engineering, vol. 2, no. 2, Summer 1995.

A.Mojssilović, M.V.Popović, A .N.Nešković, and A.D.Popović, “Wavelet Image Extension for Analysis and Classification of Infarcted Myocardial Tissue.” IEEE Trans. on Biomedical Engineering, vol. 44, no. 9, pp.856-866, September 1997.

. A.L.Amer, A.Ertüzün, and A.Erçil, “An Efficient Method for Texture Defect Detection: Subband domain Co-Occurrence Matrices.” Image and Vision Computing, Vol.18/6-7, pp.543-553, May 2000.

. Z.Shaohua, “Wavelet-Based Texture Retrieval and Modeling Visual Texture Perception.” Thesis, Department of Electrical Engineering, National University of Singapore, 2000.

. T.Ojala, M.Pietikäinen, and T.Mäenpää, “Multiresolution Gray-Scale and Resolution Invariant Texture Classification with Local Binary Pattern,” IEEE Trans. on Pattern Analysis and Machine Inteligence, vol. 24, no. 7, pp.971-987, July 2002.

. E.Chiu, J.Vaisey and M.S.Atkins, ”Wavelet Based Space-Frequency Compression of Ultrasound Images,” School of Engineering Science, School of Computing Science Simon Fraser University, Burnaby, BC, V5A IS6, Canada, February, 2001.

. M.Partio, B.Cramariuc, M.Gabbouj and A.Visa, “Rock Texture Retrieval using Gray Level Co-occurrence Matrix.” Tempere University of Tecnology, Tempere, Finland.


  • There are currently no refbacks.