Color detection in dermoscopic images of pigmented skin lesions through computer vision techniques

This thesis offers an insight into skin cancer detection, focusing on the extraction of distinct features (color, namely) from potential melanoma lesions. The following document provides an outlook of melanoma analysis, as well as experimental results based on Matlab implementations. The relevance of the work carried out throughout this project resides in the specificity of the study: color is a key characteristic in melanoma inspection. It is usually linked to pattern analysis but seldom the sole object of research. Most lines of work in the field of skin cancer diagnosis associate color with other features such as texture, shape, asymmetry or pattern of the lesion. Studies cement this belief regarding the vital significance of color, as the number of colors in a lesion happens to be the most significant biomarker for determining malignancy. Different image processing techniques will be applied to build statistical models that shape the outcome of the prospective diagnosis. The purpose of the project is the development of an assisting tool able to detect the most prevalent colors in skin pigmented lesions, in order to give a probabilistic result. The strength of this idea lies in the resemblance to actual medical procedures; dermatologists examine color to diagnose melanoma. Simulating medical proceedings is a burgeoning trend in CAD systems because it renders the advancements in this field more likely to be accepted by the medical community. An additional motivation comes from real-life statistics: skin cancer is, by far, the most frequent type of cancer. Moreover, although melanoma is the least common form of skin cancer at only around 1% of all cases, the majority of deaths related to skin cancer are due to melanoma. Furthermore, the rate of melanoma occurrence is particularly high in Spain and has significantly increased in the last decade, hence the importance of reliable diagnosis that is not exclusively contingent on the specialist’s subjective judgment.Ingeniería de Sistemas Audiovisuale

Robust Distributed Parameter Estimation in Wireless Sensor Networks

abstract: Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities. Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense. Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis. Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

Fetal ECG Extraction from Maternal ECG using Attention-based CycleGAN

Non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal. to map MECG to FECG efficiently. The high correlation between maternal and fetal ECG parts decreases the performance of convolution layers. Therefore, the masking region of interest using the attention mechanism is performed for improving signal generators' precision. The sine activation function is also used since it could retain more details when converting two signal domains. Three available datasets from the Physionet, including A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, are used to evaluate the performance. The proposed method could map abdominal MECG to scalp FECG with an average 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on A&D FECG dataset. Moreover, it achieved 99.7 % F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on, A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. These results are comparable to the state-of-the-art; thus, the proposed algorithm has the potential of being used for high-performance signal-to-signal conversion

CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey

Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.