Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity

Improved Denoising Method for Ultrasonic Echo with Mother Wavelet Optimization and Best-Basis Selection

Weak features of ultrasonicnondestructive test signals are usually immersed in noisy signals. So, in this paper, we proposed an improved scheme for noise reduction and feature extraction based on discrete wavelet transform. The basis of the mother wavelet was selected to be matched to a given signal. Three different constraints were presented to minimize the error between the denoised and the given signal. It should be mentioned that such an optimum wavelet can represent the signal more compactly with a few large coefficients which can be considered as the signal features. Standard signals and simulated ultrasonic echo were used to evaluate the performance of the presented algorithms. Signal to error ratio was used to compare the designed wavelet performance with that of standard wavelets. Simulation results revealed that the proposed method outperformed the other presented methods and even standard wavelets. The results also has shown that the signal-based noise reduction algorithms make the feature extraction more reliable. Finally, the performance of the proposed algorithm was compared with other methods from different literatures

Reduce The Noise in Speech Signals Using Wavelet Filtering

 تنخفض قدرة قنوات البيانات غالبا ما بسبب الضوضاء وتشوه الإشارات المرسلة . يستخدم تخفيض الضوضاء في مجالات  مختلفة (حيث لا يمكن عزل الإشارات المرسلة من الضوضاء والتشويه): في التعرف على الكلام ومعالجة الصور وأنظمة الاتصالات المتنقلة ومعالجة الإشارات الطبية والأنظمة الراديوية والرادارية وما إلى ذلك. توضح هذه الورقة مشكلة وجود ضوضاء في إشارات الكلام. ويتم النظر في نموذج ضوضاء غوسية بيضاء مضافة وإضافته إلى إشارة الكلام - نمذجة عملية الضوضاء. حيث تم   دراسة الميزات الاساسية للمويجات المستخدمة لتقليل الضوضاء. وتم النظر في الخوارزمية الاساسية لعميلة  ازالة الضوضاء باستخدام تقنيات تحليل المويجات. تم بناء التنفيذ العملي للحد من الضوضاء. تم رسم الاشارة الاصلية والاشاره المشوهة  والاشاره المستخلصه بعد تقليل الضوضاء. تم تحليل نتائج إلغاء الضوضاء باستخدام أسر مختلفة من المويجات، حيث تم رسم الاشكال للصلات المتبادلة بين إشارات الكلام المشوهة والنظيفة.تقليل الضوضاء نفذ باستخدام برنامج ماتلاب.The capacity of the data channels is often reduced due to noise and distortion of the transmitted signals. Noise reduction is used in various areas (where from noise and distortion the transmitted signals cannot be isolated): speech / speaker recognition, image processing, mobile communication systems, medical signal processing, radio and radar systems, etc. This paper illustrates the problem of the presence of noise in speech signals. A model of additive white Gaussian noise is considered and adding it to the speech signal – modeling of noise process. The main features of wavelets, which used in noise reduction, are described. The main algorithm of the noise cancellation process using wavelet analysis techniques is considered. Carried out the practical implementation of noise reduction. The graphs of the original, noisy and cleaned signals are plotted. An analysis of the results of noise cancellation was carried out using different families of wavelets, graphs of the cross correlation of noisy and clean speech signals are plotted. Noise reduction carried out using Matlab programing

A multi-wavelet based technique for calculating dense 2D disparity maps from stereo

A vision based approach for calculating accurate 3D models of the objects is presented. Generally industrial visual inspection systems capable of accurate 3D depth estimation rely on extra hardware tools like laser scanners or light pattern projectors. These tools improve the accuracy of depth estimation but also make the vision system costly and cumbersome. In the proposed algorithm, depth and dimensional accuracy of the produced 3D depth model depends on the existing reference model instead of the information from extra hardware tools. The proposed algorithm is a simple and cost effective software based approach to achieve accurate 3D depth estimation with minimal hardware involvement. The matching process uses the well-known coarse to fine strategy, involving the calculation of matching points at the coarsest level with consequent refinement up to the finest level. Vector coefficients of the wavelet transform-modulus are used as matching features, where wavelet transform-modulus maxima defines the shift invariant high-level features with phase pointing to the normal of the feature surface. The technique addresses the estimation of optimal corresponding points and the corresponding 2D disparity maps leading to the creation of accurate depth perception model. <br /

EKG De-noising using 1-D Wavelets Techniques

© ASEE 2009The electrocardiogram (ECG or EKG) is a graphic recording or display of the time- variant voltages produced by the myocardium during the cardiac cycle. The P, QRS, and T waves reflect the rhythmic electrical depolarization and re-polarization of the myocardium associated with the contractions of the atria and ventricles. The electrocardiogram is generally used clinically in diagnosing various diseases and conditions associated with the heart. It also serves as a timing reference for other measurements. Hence its accurate measurement is a must. A normal EKG waveform consists of common mode noises such as dc electrode offset potential and 50 or 60 Hz ac-induced interference. This paper presents the study of filtering these noises using 1-Dimensional wavelets theory. Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale. They have advantages over traditional Fourier methods in analyzing physical situations where the signal contains discontinuous and sharp spikes

Maximum Energy Subsampling: A General Scheme For Multi-resolution Image Representation And Analysis

Image descriptors play an important role in image representation and analysis. Multi-resolution image descriptors can effectively characterize complex images and extract their hidden information. Wavelets descriptors have been widely used in multi-resolution image analysis. However, making the wavelets transform shift and rotation invariant produces redundancy and requires complex matching processes. As to other multi-resolution descriptors, they usually depend on other theories or information, such as filtering function, prior-domain knowledge, etc.; that not only increases the computation complexity, but also generates errors. We propose a novel multi-resolution scheme that is capable of transforming any kind of image descriptor into its multi-resolution structure with high computation accuracy and efficiency. Our multi-resolution scheme is based on sub-sampling an image into an odd-even image tree. Through applying image descriptors to the odd-even image tree, we get the relative multi-resolution image descriptors. Multi-resolution analysis is based on downsampling expansion with maximum energy extraction followed by upsampling reconstruction. Since the maximum energy usually retained in the lowest frequency coefficients; we do maximum energy extraction through keeping the lowest coefficients from each resolution level. Our multi-resolution scheme can analyze images recursively and effectively without introducing artifacts or changes to the original images, produce multi-resolution representations, obtain higher resolution images only using information from lower resolutions, compress data, filter noise, extract effective image features and be implemented in parallel processing

Offline and real time noise reduction in speech signals using the discrete wavelet packet decomposition

This thesis describes the development of an offline and real time wavelet based speech enhancement system to process speech corrupted with various amounts of white Gaussian noise and other different noise types