Contourlet Transform for Texture Representation of Ultrasound Thyroid Images

Texture representation of ultrasound (US) images is currently considered a major issue in medical image analysis. This paper investigates the texture representation of thyroid tissue via features based on the Contourlet Transform (CT) using different types of filter banks. A variety of statistical texture features based on CT coefficients, have been considered through a selection schema. The Sequential Float Feature Selection (SFFS) algorithm with a k-NN classifier has been applied in order to investigate the most representative set of CT features. For the experimental evaluation a set of normal and nodular ultrasound thyroid textures have been utilized. The maximum classification accuracy was 93%, showing that CT based texture features can be successfully applied for the representation of different types of texture in US thyroid images.

[1]  Mark J. T. Smith,et al.  A filter bank for the directional decomposition of images: theory and design , 1992, IEEE Trans. Signal Process..

[2]  D. D.-Y. Po,et al.  Directional multiscale modeling of images using the contourlet transform , 2006, IEEE Transactions on Image Processing.

[3]  Panagiotis Tsakanikas,et al.  Improving 2‐DE gel image denoising using contourlets , 2009, Proteomics.

[4]  Bedrich J. Hosticka,et al.  A comparison of texture feature extraction using adaptive gabor filtering, pyramidal and tree structured wavelet transforms , 1996, Pattern Recognit..

[5]  M. Do.  Directional multiresolution image representations , 2002 .

[6]  Vidya B. Manian,et al.  Texture classification using logical operators , 2000, IEEE Trans. Image Process..

[7]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[8]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[9]  Constantine Kotropoulos,et al.  Fast sequential floating forward selection applied to emotional speech features estimated on DES and SUSAS data collections , 2006, 2006 14th European Signal Processing Conference.

[10]  Zhe Liu.  Minimum Distance Texture Classification of SAR Images in Contourlet Domain , 2008, 2008 International Conference on Computer Science and Software Engineering.

[11]  T. Lonnestad.  A new set of texture features based on the Haar transform , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[12]  B. N. Chatterji,et al.  Content Based Image Retrieval using Contourlet Transform , 2007 .

[13]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[14]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[15]  Dimitris A. Karras,et al.  Image compression using the wavelet transform on textural regions of interest , 1998, Proceedings. 24th EUROMICRO Conference (Cat. No.98EX204).

[16]  John Benjafield,et al.  Cognition, 3rd ed. , 2007 .

[17]  M. Vetterli.  Multi-dimensional sub-band coding: Some theory and algorithms , 1984 .

[18]  Lav R. Varshney,et al.  Despeckling Synthetic Aperture Radar Imagery using the Contourlet Transform , 2004 .

[19]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[20]  Minh N. Do,et al.  Contourlets: a directional multiresolution image representation , 2002, Proceedings. International Conference on Image Processing.

[21]  C. H. Chen,et al.  Handbook of Pattern Recognition and Computer Vision , 1993 .

[22]  Pavel Pudil,et al.  Statistical approach to pattern recognition: Theory and practical solution by means of PREDITAS system , 1991, Kybernetika.