A Comparison of Wavelet-Based and Ridgelet-Based Texture Classification of Tissues in Computed Tomography

Abstract

The research presented in this article is aimed at developing an automated imaging system for classification of tissues in medical images obtained from Computed Tomography (CT) scans. The article focuses on using multi-resolution texture analysis, specifically: the Haar wavelet, Daubechies wavelet, Coiflet wavelet, and the ridgelet. The algorithm consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The classification step is implemented using a cross-validation Classification and Regression Tree approach. A comparison of wavelet-based and ridgelet-based algorithms is presented. Tests on a large set of chest and abdomen CT images indicate that, among the three wavelet-based algorithms, the one using texture features derived from the Haar wavelet transform clearly outperforms the one based on Daubechies and Coiflet transform. The tests also show that the ridgelet-based algorithm is significantly more effective and that texture features based on the ridgelet transform are better suited for texture classification in CT medical images.

Denoising of Digital Images using Consolidation of Edges and Separable Wavelet Transform

“…Wavelet transform preserves both, the time domain and frequency domain information and also the relation between them. Wavelet theory has so far been widely used for edge detection [2], denoising [5], signal processing using hidden Markov models [8], image enhancement [6], pattern classification [9], pattern recognition [10]. The success of Wavelet transform was mainly due to its good performance in both the dimensions at varying resolutions; but one at a time [8] due to its orthogonality.…”

“…Since noise usually consists of high frequency content, thresholding or partial truncating of details coefficients, followed by reconstruction, leads to suppression of overall noise, at the cost of some distortion. Many techniques using wavelet-based thresholding have been reported in literature [6][7], [9], [13], [17], [21].…”

Textural Feature Extraction and Classification of Mammogram Images using CCCM and PNN

“…Model based methods are based on prior models such as Markov random fields [10], autoregressive models and fractals [9]. Signal processing methods are based on pixel characteristics or image frequency spectra including Law"s texture measures, Gabor filtering [8] and Wavelets [11], [12]. In the proposed method the efficiency of contourlet transform is exploited to achieve a more efficient mammogram texture feature analysis and PNN is used for classification.…”

Curvelet-Based Texture Classification of Tissues in Computed Tomography

“…The research presented in this article is part of an ongoing project [1] - [3] aimed at developing an automated imaging system for classification of tissues in medical images obtained by Computed Tomography (CT) scans. Classification of human organs in CT scans using shape or gray level information is particularly challenging due to the changing shape of organs in a stack of slices in 3D medical images and the gray level intensity overlap in soft tissues.…”

“…Following the recent introduction of the ridgelet transform, the authors proposed a classification algorithm, which uses ridgelet-based texture features [3]. This research is extended to include texture features based on the discrete curvelet transform [8].…”