Kidney stones are renal calculi that are formed due to the collection of calcium and uric acid. The major symptom for the existence of these renal calculi is severe pain, especially when it travels down the urethras To detect these renal calculi, ultrasound images are preferable. But these images have speckle noise which makes the detection of stone challenge. To obtain better results, Semantic Object Region and Morphological Analysis (SORAMA) found to be productive. First scanned image undergoes noise removal process Later the image is enhanced. Detection of Region of interest (ROI) in the image is done. Later it undergoes Dilation and Erosion were a part of Morphological analysis which produces a smoothening effect on the image. From the smoothened image, the stone is detected. If the stone is not detected then it again undergoes noise removal technique and the whole process is repeated until the smoothened image with the stone is detected. This novel research paper will be a boon to medical patients suffering from this disease to be detected and diagnose at a very early stage. 

Speckle Noise Removal by SORAMA Segmentation in Digital Image Processing to Facilitate Precise Robotic Surgery

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.

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Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

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A Novel Method for Despeckling of Ultrasound Images Using Cellular Automata-Based Despeckling Filter

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.

Towards to Optimal Wavelet Denoising Scheme-A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Medical field is enriched with various imaging modalities that help experts for analyzing and diagnosis of the diseases. Among them, Computer Tomography(CT), X-rays, Magnetic Resonance Imaging(MRI), Ultrasound(US) are popular practices that have great impact on the lives. Almost all the medical images are contaminated by noise while capturing the image and they pose huge difficulty for the interpretation of the details in the images. Automating various medical image tasks including prediction, classification, segmentation of certain region of interests require preprocessing of these images. Major task in preprocessing steps are noise removal and image enhancement. Ultrasonography is a non-invasive and inexpensive technique for obtaining cross sectional views of the different organs, visualizing blood vessels, tissues and detecting abnormal masses. Speckle noise intrinsically present in ultrasound images, are multiplicative unlike additive Gaussian noise. Images corrupted with noise degrades the image quality and reduces the accuracy in their interpretation. This paper is a comparative study on various denoising techniques that reduce the speckle noise present in B-mode US images. Different image quality metrics like Speckle suppression index(SSI), Peak signal to noise ratio(PSNR), and Structural similarity index measure(SSIM) are used to effectively measure the performance of the methods discussed.

Performance Analysis of Speckle Reduction Filtering algorithms in B-Mode Ultrasound Images

In recent years, many studies have examined filters for eliminating or reducing speckle noise, which is inherent to ultrasound images, in order to improve the metrological evaluation of their biomedical applications. In the case of medical ultrasound images, said noise can produce uncertainty in the diagnosis because details, such as limits and edges, should be preserved. Most algorithms can eliminate speckle noise, but they do not consider the conservation of these details. This paper describes, in detail, 27 techniques that mainly focus on the smoothing or elimination of speckle noise in medical ultrasound images. The aim of this study is to highlight the importance of improving said smoothing and elimination, which are directly related to several processes (such as the detection of regions of interest) described in other articles examined in this study. Furthermore, the description of this collection of techniques facilitates the implementation of evaluations and research with a more specific scope. This study initially covers several classical methods, such as spatial filtering, diffusion filtering, and wavelet filtering. Subsequently, it describes recent techniques in the field of machine learning focused on deep learning, which are not yet well known but greatly relevant, along with some modern and hybrid models in the field of speckle-noise filtering. Finally, five Full-Reference (FR) distortion metrics, common in filter evaluation processes, are detailed along with a compensation methodology between FR and Non-Reference (NR) metrics, which can generate greater certainty in the classification of the filters by considering the information of their behavior in terms of perceptual quality provided by NR metrics.

Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical applications: an Overview

The atrial fibrillation (AF) is the most common arrhythmia, which generates the highest costs on clinical systems. Theory of the rotor is one of the most recent approaches to explain the mechanisms that maintain AF. The most promising treatment is the ablation, whose success depends on rotor tip location. In a previous research, the approximate entropy (ApEn) calculated on simulated electrograms from atrial models has shown high capability for detecting the rotor tip, however it needed a human final adjustment. In addition, this technique involves a high computational cost, which is a problem for its effective application. In this study, multiple features maps were generated and different combinations of them were conducted using wavelet image fusion. The rotor tip location when using image fusion, was similar to the results achieved with the methodology based on ApEn, however, our methodology did not require any manual adjustment, and the computational cost was reduced to 85%. This study includes a comparative analysis between unipolar and bipolar electrograms obtained from a simulated 2D model of a human atrial tissue under chronic AF.

Comparison between unipolar and bipolar electrograms for detecting rotor tip from 2D fibrillation model using image fusion. A simulation study

Breast MRI interpretation requires that radiologists examine several images, depending on the acquisition protocol that is managed in the health institution, a very subjective and time-consuming process that reports large variability, which affects the final diagnosis and prognosis of the patient. In this paper, we present a computational method for classifying lesions detected in breast MRI studies, which aims to reduce physician subjectivity. The proposed approach take advantage of the ability of the Multiple Kernel Learning (MKL) strategy for optimally fusing the features extracted from the different image sequences that compose a breast MRI Study, which describe the grey level distribution of the original image and its saliency map, computed using the Graph-based Visual Saliency (GBVS) algorithm. Breast lesions were classified as positive and negative findings with an accuracy of \(85.5\%\) and \(84.8\%\) when nine and five sequences were used, respectively.

Carlos Andrés Duarte-Salazar

Papers

Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical applications: an Overview

Comparison between unipolar and bipolar electrograms for detecting rotor tip from 2D fibrillation model using image fusion. A simulation study

Breast Lesion Discrimination Using Saliency Features from MRI Sequences and MKL-Based Classification