531 research outputs found

    Computer-based estimation of circulating blood volume from ultrasound imagery

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    Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions including sepsis, trauma, dialysis and congestive heart failure. In recent years, ultrasound images of inferior vena cava (IVC) and internal jugular vein (IJV) have been used to assess volume status and guide fluid administration. This approach has limitations in that a skilled operator must perform repeated measurements over time. In this dissertation, we develop semi-automatic image processing algorithms for estimation and tracking of the IVC anterior-posterior (AP)-diameter and IJV crosssectional area in ultrasound videos. The proposed algorithms are based on active contours (ACs), where either the IVC AP-diameter or IJV CSA is estimated by minimization of an energy functional. More specifically, in chapter 2, we propose a novel energy functional based on the third centralized moment and show that it outperforms the functionals that are traditionally used with active contours (ACs). We combine the proposed functional with the polar contour representation and use it for segmentation of the IVC. In chapters 3 and 4, we propose active shape models based on ellipse; circle; and rectangles fitted inside the IVC as efficient, consistent and novel approaches to tracking and approximating the anterior-posterior (AP)-diameter even in the context of poor quality images. The proposed algorithms are based on a novel heuristic evolution functional that works very well with ultrasound images. In chapter 3, we show that the proposed active circle algorithm accurately, estimates the IVC AP-diameter. Although the estimated AP-diameter is very close to its actual value, the clinicians define the IVC AP-diameter as the largest vertical diameter of the IVC contour which deviates from its actual definition. To solve this problem and estimate the AP-diameter in the same way as its clinical definition, in chapter 4, we propose the active rectangle algorithm, where clinically measured AP-diameter is modeled as the height of a vertical thin rectangle. The results show that the AP-diameter estimated by the active rectangle algorithm is closer to its clinically measurement than the active circle and active ellipse algorithms. In chapter 5, we propose a novel adaptive polar active contour (Ad-PAC) algorithm for the segmentation and tracking of the IJV in ultrasound videos. In the proposed algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results of segmentation in previous frames. The Ad-PAC algorithm has been applied to 65 ultrasound videos and shown to be a significant improvement over existing segmentation algorithms. So far, all proposed algorithms are semi-automatic as they need an operator to either locate the vessel in the first frame, or manually segment the first first and work automatically for the next frames. In chapter 6, we proposed a novel algorithm to automatically locate the vessel in ultrasound videos. The proposed algorithm is based on convolutional neural networks (CNNs) and is trained and applied for IJV videos. In this chapter we show that although the proposed algorithm is trained for data acquired from healthy subjects, it works efficiently for the data collected from coronary heart failure (CHF) patients without additional training. Finally, conclusions are drawn and possible extensions are discussed in chapter 7

    FPGA-Based Portable Ultrasound Scanning System with Automatic Kidney Detection

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    Bedsides diagnosis using portable ultrasound scanning (PUS) offering comfortable diagnosis with various clinical advantages, in general, ultrasound scanners suffer from a poor signal-to-noise ratio, and physicians who operate the device at point-of-care may not be adequately trained to perform high level diagnosis. Such scenarios can be eradicated by incorporating ambient intelligence in PUS. In this paper, we propose an architecture for a PUS system, whose abilities include automated kidney detection in real time. Automated kidney detection is performed by training the Viola–Jones algorithm with a good set of kidney data consisting of diversified shapes and sizes. It is observed that the kidney detection algorithm delivers very good performance in terms of detection accuracy. The proposed PUS with kidney detection algorithm is implemented on a single Xilinx Kintex-7 FPGA, integrated with a Raspberry Pi ARM processor running at 900 MHz

    Penghasilan perisian sistem pakar mendiagnosis kerosakan komputer (CoFDES) dan kepenggunaannya dalam membantu staf pentadbiran Kolej Universiti Teknologi Tun Hussein Onn (KUiTTHO) membaikpulih komputer secara kendiri

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    Komputer merupakan medium yang paling penting dalam bidang teknologi maklumat. Kebanyakan penduduk dunia hari ini mempunyai komputer pcribadi di rumah mereka. Namun begitu, pemahkah kita tcrfikir bahawa komputcr hanyalah sejenis mesin yang sememangnya tidak dapat lari daripada kcrosakan. Kcrosakan yang sering terjadi adalah pelbagai, daripada komponen monitor schinggalah kcpada komponen tetikus. Oleh yang demikian, kajian ini dijalankan bertujuan untuk menghasilkan satu perisian Sistcm Pakar Mendiagnosis Kerosakan Komputer (CoFDES) dan kepenggunaannya dalam membantu stafpentadbiran Kolej Universiti Teknologi Tun Hussein Onn (KUiTTHO) membaikpulih komputer secara kendiri. Peri sian sistem CoFDES yang dihasilkan ini dibangun menggunakan peri sian Microsoft Visual Basic 6.0. Seramai 40 responden atau 25 peratus daripada populasi kajian yang terdiri daripada pembantu tadbir daripada beberapa jabatan dan fakulti di KUiTTHO, telah dipilih bagi tujuan penilaian peri sian yang dibangunkan. Maklum balas diperolehi melalui soal selidik scrta temubual telah dianalisis menggunakan perisian Statistical Package for Social Sciencc (SPSS) versi 11.0 dan perisian Microsoft Excel XP. Hasil dapatan kajian menunjukkan penggunaan sistem CoFDES telah mencapai kehendak objeki:if. Pada bab akhir kajian ini, beberapa cadangan telah dikemukakan untuk mempertingkatkan lagi kecekapan sistem CoFDES di masa hadapan

    Fully-automated tongue detection in ultrasound images

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    Tracking the tongue in ultrasound images provides information about its shape and kinematics during speech. In this thesis, we propose engineering solutions to better exploit the existing frameworks and deploy them to convert a semi-automatic tongue contour tracking system to a fully-automatic one. Current methods for detecting/tracking the tongue require manual initialization or training using large amounts of labeled images. This work introduces a new method for extracting tongue contours in ultrasound images that requires no training nor manual intervention. The method consists in: (1) application of a phase symmetry filter to highlight regions possibly containing the tongue contour; (2) adaptive thresholding and rank ordering of grayscale intensities to select regions that include or are near the tongue contour; (3) skeletonization of these regions to extract a curve close to the tongue contour and (4) initialization of an accurate active contour from this curve. Two novel quality measures were also developed that predict the reliability of the method so that optimal frames can be chosen to confidently initialize fully automated tongue tracking. This is achieved by automatically generating and choosing a set of points that can replace the manually segmented points for a semi-automated tracking approach. To improve the accuracy of tracking, this work also incorporates two criteria to re-set the tracking approach from time to time so the entire tracking result does not depend on human refinements. Experiments were run on 16 free speech ultrasound recordings from healthy subjects and subjects with articulatory impairments due to Steinert’s disease. Fully automated and semi automated methods result in mean sum of distances errors of 1.01mm±0.57mm and 1.05mm± 0.63mm, respectively, showing that the proposed automatic initialization does not significantly alter accuracy. Moreover, the experiments show that the accuracy would improve with the proposed re-initialization (mean sum of distances error of 0.63mm±0.35mm)

    Vision-based estimation of volume status in ultrasound

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    This thesis provides a proof-of-concept approach to the analysis of ultrasound imagery using machine learning and computer vision for the purposes of tracking relative changes in apparent circulating blood volume. Data for the models was collected from a simulation which involved having healthy subjects recline at angles between 0 and 90 degrees to induce changes in the size of the internal jugular vein (IJV) resulting from gravity. Ultrasound video clips were then captured of the IJV. The clips were segmented, followed by feature generation, feature selection and training of predictive models to determine the angle of inclination. This research provides insight into the feasibility of using automated analysis techniques to enhance portable ultrasound as a monitoring tool. In a dataset of 34 subjects the angle was predicted within 11 degrees. An accuracy of 89% was achieved for high/low classification

    Robust density modelling using the student's t-distribution for human action recognition

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    The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE
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