90 research outputs found

    Multi texture analysis of colorectal cancer continuum using multispectral imagery

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    Purpose This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. Materials and Methods In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models. Results Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%. Conclusions These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images

    Towards to optimal wavelet denoising scheme - A novel spatial and volumetric mapping of wavelet-based biomedical data smoothing

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    Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.Web of Science2018art. no. 530

    N-way modeling for wavelet filter determination in Multivariate Image Analysis

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    Additional information may be found in the online version of this article at the publisher’s web site[EN] When trying to analyze spatial relationships in image analysis, wavelets appear as one of the state-of-the-art tools. However, image analysis is a problem-dependent issue, and different applications might require different wavelets in order to gather the main sources of variation in the acquired images with respect to the specific task to be performed. This paper provides a methodology based on N-way modeling for properly selecting the best wavelet choice to use or at least to provide a range of possible wavelet choices (in terms of families, filters, and decomposition levels), for each image and problem at hand. The methodology has been applied on two different data sets with exploratory and monitoring objectives. Copyright © 2015 John Wiley & Sons, Ltd.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02.Prats-Montalbán, JM.; Cocchi, M.; Ferrer Riquelme, AJ. (2015). N-way modeling for wavelet filter determination in Multivariate Image Analysis. Journal of Chemometrics. 29:379-388. https://doi.org/10.1002/cem.2717S37938829Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Liu, J. J., & MacGregor, J. F. (2007). On the extraction of spectral and spatial information from images. Chemometrics and Intelligent Laboratory Systems, 85(1), 119-130. doi:10.1016/j.chemolab.2006.05.011Liu, J. J., & MacGregor, J. F. (2006). Estimation and monitoring of product aesthetics: application to manufacturing of «engineered stone» countertops. Machine Vision and Applications, 16(6), 374-383. doi:10.1007/s00138-005-0009-8Reis, M. S., & Bauer, A. (2009). Wavelet texture analysis of on-line acquired images for paper formation assessment and monitoring. Chemometrics and Intelligent Laboratory Systems, 95(2), 129-137. doi:10.1016/j.chemolab.2008.09.007Van de Wouwer G Wavelets for multiscale texture analysis 1998Rackov, D. M., Popovic, M. V., & Mojsilovic, A. (2000). On the selection of an optimal wavelet basis for texture characterization. IEEE Transactions on Image Processing, 9(12), 2043-2050. doi:10.1109/83.887972Villasenor, J. D., Belzer, B., & Liao, J. (1995). Wavelet filter evaluation for image compression. IEEE Transactions on Image Processing, 4(8), 1053-1060. doi:10.1109/83.403412Svensson, O., Abrahamsson, K., Engelbrektsson, J., Nicholas, M., Wikström, H., & Josefson, M. (2006). An evaluation of 2D-wavelet filters for estimation of differences in textures of pharmaceutical tablets. Chemometrics and Intelligent Laboratory Systems, 84(1-2), 3-8. doi:10.1016/j.chemolab.2006.04.019Engelbrektsson, J., Abrahamsson, K., Breitholtz, J., Nicholas, M., Svensson, O., Wikström, H., & Josefson, M. (2010). The impact of Mexican hat and dual-tree complex wavelet transforms on multivariate evaluation of image texture properties. Journal of Chemometrics, 24(7-8), 454-463. doi:10.1002/cem.1285Liu, J. J., & MacGregor, J. F. (2005). Modeling and Optimization of Product Appearance:  Application to Injection-Molded Plastic Panels. Industrial & Engineering Chemistry Research, 44(13), 4687-4696. doi:10.1021/ie0492101Mallet, Y., Coomans, D., Kautsky, J., & De Vel, O. (1997). Classification using adaptive wavelets for feature extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(10), 1058-1066. doi:10.1109/34.625106Henrion, R. (1994). N-way principal component analysis theory, algorithms and applications. Chemometrics and Intelligent Laboratory Systems, 25(1), 1-23. doi:10.1016/0169-7439(93)e0086-jMallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693. doi:10.1109/34.192463Pesquet, J.-C., Krim, H., & Carfantan, H. (1996). Time-invariant orthonormal wavelet representations. IEEE Transactions on Signal Processing, 44(8), 1964-1970. doi:10.1109/78.533717Coifman, R. R., & Donoho, D. L. (1995). Translation-Invariant De-Noising. Lecture Notes in Statistics, 125-150. doi:10.1007/978-1-4612-2544-7_9Juneau, P.-M., Garnier, A., & Duchesne, C. (2015). The undecimated wavelet transform–multivariate image analysis (UWT-MIA) for simultaneous extraction of spectral and spatial information. Chemometrics and Intelligent Laboratory Systems, 142, 304-318. doi:10.1016/j.chemolab.2014.09.007Daubechies I Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics 1992Jawerth, B., & Sweldens, W. (1994). An Overview of Wavelet Based Multiresolution Analyses. SIAM Review, 36(3), 377-412. doi:10.1137/1036095Gurden, S. P., Westerhuis, J. A., Bro, R., & Smilde, A. K. (2001). A comparison of multiway regression and scaling methods. Chemometrics and Intelligent Laboratory Systems, 59(1-2), 121-136. doi:10.1016/s0169-7439(01)00168-xWesterhuis, J. A., Kourti, T., & MacGregor, J. F. (1999). Comparing alternative approaches for multivariate statistical analysis of batch process data. Journal of Chemometrics, 13(3-4), 397-413. doi:10.1002/(sici)1099-128x(199905/08)13:3/43.0.co;2-iBro, R., & Smilde, A. K. (2003). Centering and scaling in component analysis. Journal of Chemometrics, 17(1), 16-33. doi:10.1002/cem.773Henrion, R., & Andersson, C. A. (1999). 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    Coastal Hurricane Damage Assessment via Wavelet Transform of Remotely Sensed Imagery

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    This dissertation uses post storm imagery processed using wavelet transforms to investigate the capability of wavelet transform-based methods to classify post storm damage of residential areas. Five level Haar, Meyer, Symlets, and Coiflets wavelet transform decompositions of the post storm imagery are inputs to damage classification models of post hurricane and tornado damage. Hurricanes Ike, Rita, Katrina, and Ivan are examined as are the 2011 Joplin and Tuscaloosa tornadoes. Wavelet transform-based classification methods yielded varying classification accuracies for the four hurricanes examined, ranging from 67 percent to 89 percent classification accuracy for classification models informed by samples from the storms classified. Classification accuracies fall when the samples being classified are from a hurricane not informing the classification model, from 17 percent for Rita classified with an Ike-based model, 41 percent for Rita classified with an Ike-Katrina-based model, to 69 percent for Rita classified with an Ike-Katrina-Ivan-based model. The variability within and poor classification accuracy of these models can be attributed to the large variations in the four hurricane events studied and the significant differences in impacted land cover for each of these storms. Classification accuracies improved when these variations were limited via examination of residential areas impacted by 2011 Joplin and Tuscaloosa tornadoes. Damage classification models required as few as nineteen to as many as fifty nine wavelet coefficients to explain the variability in the hurricane storm data samples, and included all four wavelet functions studied. A similar analysis of the tornado damaged areas resulted in a damage classification model with only six wavelet coefficients, four Meyer-based, one Symlets-based and one Haar-based. Classification accuracies ranged from 96 percent for samples included in the model formation to 85 percent for samples not included in the model formation. The damage classification accuracies found for tornado storms suggests this model is suitable for operational implementation. The damage classification accuracies found for the hurricane storms suggests further investigation into methods that will reduce the variability attributable to land cover and storm variability

    Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach

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    Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.This work was partially supported by by FEDER funds through MINECO project TIN2017-85827-P, and ELKARTEK funded projects ENSOL2 and CODISAVA2 (KK-202000077 and KK-202000044) supported by the Basque Governmen

    Visual Object Tracking Approach Based on Wavelet Transforms

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    In this Thesis, a new visual object tracking (VOT) approach is proposed to overcome the main challenging problem encountered within the existing approaches known as the significant appearance changes which is due mainly to the heavy occlusion and illumination variations. Indeed, the proposed approach is based on combining the deep convolutional neural networks (CNN), the histograms of oriented gradients (HOG) features, and the discrete wavelet packet transform to ensure the implementation of three ideas. Firstly, solving the problem of illumination variation by incorporating the coefficients of the image discrete wavelet packet transform instead of the image template to handle the case of images with high saturation in the input of the used CNN, whereas the inverse discrete wavelet packet transform is used at the output for extracting the CNN features. Secondly, by combining four learned correlation filters with convolutional features, the target location is deduced using multichannel correlation maps at the CNNs output. On the other side, the maximum value of the resulting maps from correlation filters with convolutional features produced by HOG feature of the image template previously obtained are calculated and which are used as an updating parameter of the correlation filters extracted from CNN and from HOG where the major aim is to ensure long-term memory of target appearance so that the target item may be recovered if tracking fails. Thirdly, to increase the performance of HOG, the coefficients of the discrete packet wavelet transform are employed instead of the image template. Finally, for the validation and the evaluation of the proposed tracking approach performance based on specific performance metrics in comparison to the state-of-the-art counterparts, extensive simulation experiments on benchmark datasets have been conducted out, such as OTB50, OTB100 , TC128 ,and UAV20. The obtained results clearly prove the validity of the proposed approach in solving the encountered problems of visual object tracking in almost the experiment cases presented in this thesis compared to other existing tracking approaches

    Wavelet-Neural Network Based Image Compression System for Colour Images

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    There are many images used by human being, such as medical, satellite, telescope, painting, and graphic or animation generated by computer images. In order to use these images practically, image compression method has an essential role for transmission and storage purposes. In this research, a wavelet based image compression technique is used. There are various wavelet filters available. The selection of filters has considerable impact on the compression performance. The filter which is suitable for one image may not be the best for another. The image characteristics are expected to be parameters that can be used to select the available wavelet filter. The main objective of this research is to develop an automatic wavelet-based colour image compression system using neural network. The system should select the appropriate wavelet for the image compression based on the image features. In order to reach the main goal, this study observes the cause-effect relation of image features on the wavelet codec (compression-decompression) performance. The images are compressed by applying different families of wavelets. Statistical hypothesis testing by non parametric test is used to establish the cause-effect relation between image features and the wavelet codec performance measurements. The image features used are image gradient, namely image activity measurement (IAM) and spatial frequency (SF) values of each colour component. This research is also carried out to select the most appropriate wavelet for colour image compression, based on certain image features using artificial neural network (ANN) as a tool. The IAM and SF values are used as the input; therefore, the wavelet filters are used as the output or target in the network training. This research has asserted that there are the cause-effect relations between image features and the wavelet codec performance measurements. Furthermore, the study reveals that the parameters in this investigation can be used for the selection of appropriate wavelet filters. An automatic wavelet-based colour image compression system using neural network is developed. The system can give considerably good results
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