Morphological detection based on size and contrast criteria. Application to cells detection

This paper deals with a detection algorithm relying on size and contrast criteria. It is suitable for a large range of applications where a priori information about the size and the contrast of the objects to detect is available. The detection is performed in three separate steps: the first one is a preprocessing which removes unuseful information with a size criterion. The second one performs a feature extraction based on contrast. Finally, the last step is the decision itself. All these steps make use of morphological transformations because of their ability to deal with the criteria of interest and of their low computational cost. As an example, this algorithm is applied to the automatic detection of spermatozoa.Peer ReviewedPostprint (published version

Statistical image processing for the detection of dermoscopic criteria

An image based system implementing a well-known diagnostic method is disclosed for the automatic detection of melanomas as support to clinicians. The software procedure is able to recognize automatically the skin lesion within the digital image, measure morphological and chromatic feature, carry out a suitable classification for the detection of structural dermoscopic criteria provided by the 7-Point Check List. Experimental results about the adoption of statistical techniques applied to the border detection, feature extraction and classification as well as the resulting diagnostic score are described with reference to a large image set. Copyright © 2011 by the International Measurement Confederation (IMEKO) All rights reserved

NuSTAR observations of X-ray bursts from the magnetar 1E 1048.1-5937

We report the detection of eight bright X-ray bursts from the 6.5-s magnetar 1E 1048.1-5937, during a 2013 July observation campaign with the Nuclear Spectroscopic Telescope Array (NuSTAR). We study the morphological and spectral properties of these bursts and their evolution with time. The bursts resulted in count rate increases by orders of magnitude, sometimes limited by the detector dead time, and showed blackbody spectra with kT=6-8 keV in the T90 duration of 1-4 s, similar to earlier bursts detected from the source. We find that the spectra during the tail of the bursts can be modeled with an absorbed blackbody with temperature decreasing with flux. The bursts flux decays followed a power-law of index 0.8-0.9. In the burst tail spectra, we detect a ~13 keV emission feature, similar to those reported in previous bursts from this source as well as from other magnetars observed with the Rossi X-ray Timing Explorer (RXTE). We explore possible origins of the spectral feature such as proton cyclotron emission, which implies a magnetic field strength of B~2X10^15 G in the emission region. However, the consistency of the energy of the feature in different objects requires further explanation.Comment: 10 pages, 6 figures, accepted for publication in Ap

Detection of visitors in elderly care using a low-resolution visual sensor network

Loneliness is a common condition associated with aging and comes with extreme health consequences including decline in physical and mental health, increased mortality and poor living conditions. Detecting and assisting lonely persons is therefore important-especially in the home environment. The current studies analyse the Activities of Daily Living (ADL) usually with the focus on persons living alone, e.g., to detect health deterioration. However, this type of data analysis relies on the assumption of a single person being analysed, and the ADL data analysis becomes less reliable without assessing socialization in seniors for health state assessment and intervention. In this paper, we propose a network of cheap low-resolution visual sensors for the detection of visitors. The visitor analysis starts by visual feature extraction based on foreground/background detection and morphological operations to track the motion patterns in each visual sensor. Then, we utilize the features of the visual sensors to build a Hidden Markov Model (HMM) for the actual detection. Finally, a rule-based classifier is used to compute the number and the duration of visits. We evaluate our framework on a real-life dataset of ten months. The results show a promising visit detection performance when compared to ground truth

Automatic Detection of Pulmonary Embolism in CTA Images Using Machine Learning

In this study, a novel computer-aided detection (CAD) method is introduced to detect pulmonary embolism (PE) in computed tomography angiography (CTA) images. This method consists of lung vessel segmentation, PE candidate detection, feature extraction, feature selection and classification of PE. PE candidates are determined in lung vessel tree. Then, feature extraction is carried out based on morphological properties of PEs. Stepwise feature selection method is used to find the best set of the features. Artificial neural network (ANN), k-nearest neighbours (KNN) and support vector machines (SVM) are used as classifiers. The CAD system is evaluated for 33 CTA datasets with 10 fold cross-validation. The sensitivities of these classifiers are obtained as 98.3 %, 57.3 % and 73 % at 10.2, 5.7 and 8.2 false positives per dataset respectively

A comprehensive overview of the Cold Spot

The report of a significant deviation of the CMB temperature anisotropies distribution from Gaussianity (soon after the public release of the WMAP data in 2003) has become one of the most solid WMAP anomalies. This detection grounds on an excess of the kurtosis of the Spherical Mexican Hat Wavelet coefficients at scales of around 10 degrees. At these scales, a prominent feature --located in the southern Galactic hemisphere-- was highlighted from the rest of the SMHW coefficients: the Cold Spot. This article presents a comprehensive overview related to the study of the Cold Spot, paying attention to the non-Gaussianity detection methods, the morphological characteristics of the Cold Spot, and the possible sources studied in the literature to explain its nature. Special emphasis is made on the Cold Spot compatibility with a cosmic texture, commenting on future tests that would help to give support or discard this hypothesis.Comment: 21 pages, 14 figures. Accepted for publication in the Advances in Astronomy special issue "Testing the Gaussianity and Statistical Isotropy of the Universe

Detection and extraction of the ECG signal parameters

This work investigates a set of efficient techniques to extract important features from the ECG data applicable in automatic cardiac arrhythmia classification. The selected parameters are divided into two main categories namely morphological and statistical features. Extraction of morphological features was achieved using signal processing techniques and detection of statistical features was performed by employing mathematical methods. Each specific method was applied to a pre-selected data segment of the MIT-BIH database. The classification of different heart beats was performed based upon the extracted features. The morphological features were found as the most efficient for further ECG signal analysis. However, because of ECG signal variability in different patients, the mathematical approach is preferred for a precise and robust feature extraction. As a result of the extracted features, an efficient computer based ECG signal classifier could be developed for detection of a vast range of cardiac arrhythmias