11,067 research outputs found

    Automated Coronal Hole Identification via Multi-Thermal Intensity Segmentation

    Full text link
    Coronal holes (CH) are regions of open magnetic fields that appear as dark areas in the solar corona due to their low density and temperature compared to the surrounding quiet corona. To date, accurate identification and segmentation of CHs has been a difficult task due to their comparable intensity to local quiet Sun regions. Current segmentation methods typically rely on the use of single EUV passband and magnetogram images to extract CH information. Here, the Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm (CHIMERA) is described, which analyses multi-thermal images from the Atmospheric Image Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) to segment coronal hole boundaries by their intensity ratio across three passbands (171 \AA, 193 \AA, and 211 \AA). The algorithm allows accurate extraction of CH boundaries and many of their properties, such as area, position, latitudinal and longitudinal width, and magnetic polarity of segmented CHs. From these properties, a clear linear relationship was identified between the duration of geomagnetic storms and coronal hole areas. CHIMERA can therefore form the basis of more accurate forecasting of the start and duration of geomagnetic storms

    Nemo: a computational tool for analyzing nematode locomotion

    Get PDF
    The nematode Caenorhabditis elegans responds to an impressive range of chemical, mechanical and thermal stimuli and is extensively used to investigate the molecular mechanisms that mediate chemosensation, mechanotransduction and thermosensation. The main behavioral output of these responses is manifested as alterations in animal locomotion. Monitoring and examination of such alterations requires tools to capture and quantify features of nematode movement. In this paper, we introduce Nemo (nematode movement), a computationally efficient and robust two-dimensional object tracking algorithm for automated detection and analysis of C. elegans locomotion. This algorithm enables precise measurement and feature extraction of nematode movement components. In addition, we develop a Graphical User Interface designed to facilitate processing and interpretation of movement data. While, in this study, we focus on the simple sinusoidal locomotion of C. elegans, our approach can be readily adapted to handle complicated locomotory behaviour patterns by including additional movement characteristics and parameters subject to quantification. Our software tool offers the capacity to extract, analyze and measure nematode locomotion features by processing simple video files. By allowing precise and quantitative assessment of behavioral traits, this tool will assist the genetic dissection and elucidation of the molecular mechanisms underlying specific behavioral responses.Comment: 12 pages, 2 figures. accepted by BMC Neuroscience 2007, 8:8

    thermogram Breast Cancer Detection : a comparative study of two machine learning techniques

    Get PDF
    Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%

    Image informatics strategies for deciphering neuronal network connectivity

    Get PDF
    Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

    Computer Vision Algorithms For An Automated Harvester

    Get PDF
    Image classification and segmentation are the two main important parts in the 3D vision system of a harvesting robot. Regarding the first part, the vision system aids in the real time identification of contaminated areas of the farm based on the damage identified using the robot’s camera. To solve the problem of identification, a fast and non-destructive method, Support Vector Machine (SVM), is applied to improve the recognition accuracy and efficiency of the robot. Initially, a median filter is applied to remove the inherent noise in the colored image. SIFT features of the image are then extracted and computed forming a vector, which is then quantized into visual words. Finally, the histogram of the frequency of each element in the visual vocabulary is created and fed into an SVM classifier, which categorizes the mushrooms as either class one or class two. Our preliminary results for image classification were promising and the experiments carried out on the data set highlight fast computation time and a high rate of accuracy, reaching over 90% using this method, which can be employed in real life scenario. As pertains to image Segmentation on the other hand, the vision system aids in real time identification of mushrooms but a stiff challenge is encountered in robot vision as the irregularly spaced mushrooms of uneven sizes often occlude each other due to the nature of mushroom growth in the growing environment. We address the issue of mushroom segmentation by following a multi-step process; the images are first segmented in HSV color space to locate the area of interest and then both the image gradient information from the area of interest and Hough transform methods are used to locate the center position and perimeter of each individual mushroom in XY plane. Afterwards, the depth map information given by Microsoft Kinect is employed to estimate the Z- depth of each individual mushroom, which is then being used to measure the distance between the robot end effector and center coordinate of each individual mushroom. We tested this algorithm under various environmental conditions and our segmentation results indicate this method provides sufficient computational speed and accuracy
    • …
    corecore