Directional wavelet based features for colonic polyp classification

Abstract

In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far. We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods. It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state-of-the-art methods on most of the databases. We will also show that the Weibull distribution is better suited to model the subband coefficient distribution than other commonly used probability distributions like the Gaussian distribution and the generalized Gaussian distribution. So this work gives a reasonable summary of wavelet based methods for colonic polyp classification and the huge amount of endoscopic polyp databases used for our experiments assures a high significance of the achieved results.

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

“…Other features were also proved to be quite suitable for colonic polyp classification as the Gabor wavelets [16], vascularization features [17], and directional wavelet transform features [18]. Particularly, in the work of Wimmer et al [18], using the same 8 colonic polyp databases of this work, an average accuracy of 80.3% was achieved in the best scenario. In this work, we achieve an average accuracy of 93.55% in our best scenario.…”

“…Despite the difference between natural and medical images, some feature descriptors designed especially for natural images are used successfully in medical image detection and classification, for example, texture-based polyp detection [3], Fourier and Wavelet filters for colon classification [18], shape descriptors [44], and local fractal dimension [45] for colonic polyp classification. Additionally, recent studies show the potential of the knowledge transfer between natural and medical images using pretrained (off-the-shelf) CNNs [34, 46].…”

“…With high-magnification colonoscopies it is possible to acquire images up to 150-fold magnified, revealing the fine surface structure of the mucosa as well as small lesions. Recent work related to classification of colonic polyps used highly-detailed endoscopic images in combination with different technologies divided into three categories: high-definition endoscope (with or without staining the mucosa) combined with the i-Scan technology (1, 2, and 3) [18], high-magnification chromoendoscopy [8], and high-magnification endoscopy combined with narrow band imaging [47]. …”

“…For example, the dual-tree complex wavelet transform DT-CWT features proved to be quite suitable for the distinction of different types of polyps as can be seen in many works like, for example, [13–15]. Other features were also proved to be quite suitable for colonic polyp classification as the Gabor wavelets [16], vascularization features [17], and directional wavelet transform features [18]. Particularly, in the work of Wimmer et al [18], using the same 8 colonic polyp databases of this work, an average accuracy of 80.3% was achieved in the best scenario.…”

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

“…The features employed belong to three categories namely, Local Phase Quantization (LPQ) [22] features, Gabor features [14] and Weibull distribution parameters of the sub-bands obtained through curvelet-based decomposition [34]. The LPQ and Gabor features corresponding to the query and enrolled images are matched using the cosine similarity measure, while the correlation score is computed for the Weibull features.…”