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Full-Text Articles in Engineering
A Scale Space Local Binary Pattern (Sslbp) – Based Feature Extraction Framework To Detect Bones From Knee Mri Scans, Jinyeong Mun
A Scale Space Local Binary Pattern (Sslbp) – Based Feature Extraction Framework To Detect Bones From Knee Mri Scans, Jinyeong Mun
Electronic Theses and Dissertations
The medical industry is currently working on a fully autonomous surgical system, which is considered a novel modality to go beyond technical limitations of conventional surgery. In order to apply an autonomous surgical system to knees, one of the primarily responsible areas for supporting the total weight of human body, accurate segmentation of bones from knee Magnetic Resonance Imaging (MRI) scans plays a crucial role. In this paper, we propose employing the Scale Space Local Binary Pattern (SSLBP) feature extraction, a variant of local binary pattern extractions, for detecting bones from knee images. The proposed methods consist of two phases. …
Feasibility Of Melville Marginalia Authorship Differentiation, Aaron Burdin
Feasibility Of Melville Marginalia Authorship Differentiation, Aaron Burdin
Boise State University Theses and Dissertations
We examine the feasibility of using image processing techniques to determine differentiation in authorship of historical pencil marks. Pencil marks with unattributed and attributed authorship are segmented from digital images of historical books. Analysis is performed on five features that are extracted from the "vertical" pencil marks, with those features used as a basis for authorship of marks. These marks consist of single stroke marks that are interspersed in the same document. We describe the challenges of the digital format that we were given and the steps taken in using autonomous segmentation to save pixel locations of marks. Five mark …
Sparse Coding Based Feature Representation Method For Remote Sensing Images, Ender Oguslu
Sparse Coding Based Feature Representation Method For Remote Sensing Images, Ender Oguslu
Electrical & Computer Engineering Theses & Dissertations
In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation systems based on the sparse signal model are computationally expensive, requiring to solve a convex optimization problem to learn a dictionary. A sparse coding feature representation framework for the classification of HSI is presented that alleviates the complexity of sparse coding through sub-band construction, dictionary learning, and encoding steps. In the framework, we construct the dictionary based upon the extracted sub-bands from the spectral representation of a pixel. In the encoding step, we utilize a soft …
Medical Image Registration Using Artificial Neural Network, Hyunjong Choi
Medical Image Registration Using Artificial Neural Network, Hyunjong Choi
Master's Theses
Image registration is the transformation of different sets of images into one coordinate system in order to align and overlay multiple images. Image registration is used in many fields such as medical imaging, remote sensing, and computer vision. It is very important in medical research, where multiple images are acquired from different sensors at various points in time. This allows doctors to monitor the effects of treatments on patients in a certain region of interest over time. In this thesis, artificial neural networks with curvelet keypoints are used to estimate the parameters of registration. Simulations show that the curvelet keypoints …
Identifying Image Manipulation Software From Image Features, Devlin T. Boyter
Identifying Image Manipulation Software From Image Features, Devlin T. Boyter
Theses and Dissertations
As technology steadily increases in the field of image manipulation, determining which software was used to manipulate an image becomes increasingly complex for law enforcement and intelligence agencies. To combat this difficult problem, new techniques that examine the artifacts left behind by a specific manipulation are converted to features for classification. This research implemented four preexisting image manipulation detection techniques into a framework of modules: Two-Dimensional Second Derivative, One-Dimensional Zero Crossings, Quantization Matrices Identification, and File Metadata analysis. The intent is the creation of a framework to develop a capability to determine which specific image manipulation software program manipulated an …
Cervical Cancer Histology Image Feature Extraction And Classification, Peng Guo
Cervical Cancer Histology Image Feature Extraction And Classification, Peng Guo
Masters Theses
"Cervical cancer, the second most common cancer affecting women worldwide and the most common in developing countries can be cured if detected early and treated. Expert pathologists routinely visually examine histology slides for cervix tissue abnormality assessment. In previous research, an automated, localized, fusion-based approach was investigated for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 62 digitized histology images obtained through the National Library of Medicine. In this research, CIN grade assessments from two pathologists are analyzed and are used to facilitate atypical cell concentration feature development …
Vocal Fold Analysis From High Speed Videoendoscopic Data, Jing Chen
Vocal Fold Analysis From High Speed Videoendoscopic Data, Jing Chen
LSU Doctoral Dissertations
High speed videoendoscopy (HSV) of the larynx far surpasses the limits of videostroboscopy in evaluating the vocal fold vibratory behavior by providing much higher frame rate. HSV enables the visualization of vocal fold vibratory pattern within an actual glottic cycle. This very detailed infor-mation on vocal fold vibratory characteristics could provide valuable information for the assessment of vocal fold vibratory function in disordered voices and the treatments effects of the behavioral, medical and surgical treatment procedures. In this work, we aim at addressing the problem of classi-fying voice disorders with varying etiology by following four steps described shortly. Our method-ology …
Experiments With Gmti Radar Using Micro-Doppler, Benjamin Walter Dilsaver
Experiments With Gmti Radar Using Micro-Doppler, Benjamin Walter Dilsaver
Theses and Dissertations
As objects move, their changing shape produces a signature that can be measured by a radar system. That signature is called the micro-Doppler signature. The micro-Doppler signature of an object is a distinguishing characteristic for certain classes of objects. In this thesis features are extracted from the micro-Doppler signature and are used to classify objects. The scope of the objects is limited to humans walking and traveling vehicles. The micro-Doppler features are able to distinguish the two classes of objects. With a sufficient amount of training data, the micro-Doppler features may be used with learning algorithms to predict unknown objects …
Feature Extraction Through K-Means Segmentation For Melanoma Detection, Snigdha Priya Bommadevara
Feature Extraction Through K-Means Segmentation For Melanoma Detection, Snigdha Priya Bommadevara
Masters Theses
"Malignant melanoma is responsible for 75% of the deaths caused due to skin cancer annually. However, melanoma detection can be possible through feature extraction and pattern classification, which can lower the risk, if the melanoma is detected at an early stage. Clustering is one of the most useful tools used to differentiate features that can contribute to melanoma. This research work uses the k-means clustering algorithm for implementation of color segmentation. However, k-means clustering requires a predefined value of k, i.e., the number of clusters must be specified at the beginning of the run. This research uses a predefined value …
Computer-Aided Detection Of Sleep Apnea And Sleep Stage Classification Using Hrv And Eeg Signals, Edson F. Estrada
Computer-Aided Detection Of Sleep Apnea And Sleep Stage Classification Using Hrv And Eeg Signals, Edson F. Estrada
Open Access Theses & Dissertations
Sleep is a circadian rhythm essential for human life. Many events occur in the body during this state. In the past, significant efforts have been made to provide clinicians with reliable and less intrusive tools to automatically classify the sleep stages and detect apnea events. A few systems are available in the market to accomplish this task. However, sleep specialists may not have full confidence and trust in such systems due to issues related to their accuracy, sensitivity and specificity. The main objective of this work is to explore possible relationships among sleep stages and apneic events and improve on …
Hybrid And Hierarchical Image Registration Techniques, Dongjiang Xu
Hybrid And Hierarchical Image Registration Techniques, Dongjiang Xu
Electronic Theses and Dissertations
A large number of image registration techniques have been developed for various types of sensors and applications, with the aim to improve the accuracy, computational complexity, generality, and robustness. They can be broadly classified into two categories: intensity-based and feature-based methods. The primary drawback of the intensity-based approaches is that it may fail unless the two images are misaligned by a moderate difference in scale, rotation, and translation. In addition, intensity-based methods lack the robustness in the presence of non-spatial distortions due to different imaging conditions between images. In this dissertation, the image registration is formulated as a two-stage hybrid …
Signal Modeling With Non-Uniform Time Sampling Of Features For Automatic Speech Recognition, Montri Karnjanadecha
Signal Modeling With Non-Uniform Time Sampling Of Features For Automatic Speech Recognition, Montri Karnjanadecha
Electrical & Computer Engineering Theses & Dissertations
This dissertation presents an investigation of non-uniform time sampling methods for spectral/temporal feature extraction in speech. Frame-based features were computed based on an encoding of the global spectral shape using a Discrete Cosine Transform. In most current “standard” methods, trajectory (dynamic) features are determined from frame-based parameters using a fixed time sampling, i.e., fixed block length and fixed block spacing. In this research, new methods are proposed and investigated in which block length and/or block spacing are variable. The idea was initially tested with HMM-based isolated word recognition, and a significant performance improvement resulted when a variable block length and …
Real Time Detection Of Anomalous Satellite Behavior Has Ground Based Telescope Images, Geoffrey S. Maron
Real Time Detection Of Anomalous Satellite Behavior Has Ground Based Telescope Images, Geoffrey S. Maron
Theses and Dissertations
Air Force analysts are faced with the task of monitoring satellites with ground based telescopes. Images are collected and analyzed in a time consuming and subjective effort to detect any behavior that is anomalous. This research maximizes use of a priori information to create an automated, real time satellite behavior classification tool. Using modeling software and knowledge of a satellite's orbit, reference imagery is created for each measured image in a satellite pass. Features are extracted from the measured and reference image pairs that provide good overall Gaussian classification accuracy (85%), reduce the dimensionality of the problem (from 32,768 down …