Browsing by Subject "Features"

Now showing 1 - 7 of 7
  • Thumbnail Image
    Item
    4-H pledge comes of age
    (University of Minnesota Extension, 1920) University of Minnesota Extension
  • Thumbnail Image
    Item
    Automated segmentation and pathology detection in ophthalmic images
    (2014-07) Roy Chowdhury, Sohini
    Computer-aided medical diagnostic system design is an emerging inter-disciplinary technology that assists medical practitioners for providing quick and accurate diagnosis and prognosis of pathology. Since manual assessments of digital medical images can be both ambiguous and time-consuming, computer-aided image analysis and evaluation systems can be beneficial for baseline diagnosis, screening and disease prioritization tasks. This thesis presents automated algorithms for detecting ophthalmic pathologies pertaining to the human retina that may lead to acquired blindness in the absence of timely diagnosis and treatment. Multi-modal automated segmentation and detection algorithms for diabetic manifestations such as Diabetic Retinopathy and Diabetic Macular Edema are presented. Also, segmentation algorithms are presented that can be useful for automated detection of Glaucoma, Macular Degeneration and Vein Occlusions. These algorithms are robust to normal and pathological images and incur low computationally complexity.First, we present a novel blood vessel segmentation algorithm using fundus images that extracts the major blood vessels by applying high-pass filtering and morphological transforms followed by addition of fine vessel pixels that are classified by a Gaussian Mixture Model (GMM) classifier. The proposed algorithm achieves more than 95% vessel segmentation accuracy on three publicly available data sets. Next, we present an iterative blood vessel segmentation algorithm that initially estimates the major blood vessels, followed by iterative addition of fine blood vessel segments till a novel stopping criterion terminates the iterative vessel addition process. This iterative algorithm is specifically robust to thresholds since it achieves 95.35% vessel segmentation accuracy with 0.9638 area under ROC curve (AUC) on abnormal retinal images from the publicly available STARE data set.We propose a novel rule-based automated optic disc (OD) segmentation algorithm that detects the OD boundary and the location of vessel origin (VO) pixel. This algorithm initially detects OD candidate regions at the intersection of the bright regions and the blood vessels in a fundus image subjected to certain structural constraints, followed by the estimation of a best fit ellipse around the convex hull that combines all the detected OD candidate regions. The centroid of the blood vessels within the segmented OD boundary is detected as the VO pixel location. The proposed algorithm results in an average of 80% overlap score on images from five public data sets.We present a novel computer-aided screening system (DREAM) that analyzes fundus images with varying illumination and fields of view, and generates a severity grade for non-proliferative diabetic retinopathy (NPDR) using machine learning. Initially, the blood vessel regions and the OD region are detected and masked as the fundus image background. Abnormal foreground regions corresponding to bright and red retinopathy lesions are then detected. A novel two-step hierarchical classification approach is proposed where the non-lesions or false positives are rejected in the first step. In the second step, the bright lesions are classified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and micro-aneurysms. Finally, the number of lesions detected per image is combined to generate a severity grade. The DReAM system achieves 100% sensitivity, 53.16% specificity and 0.904 AUC on a publicly available MESSIDOR data set with 1200 images. Additionally, we propose algorithms that detect post-operative laser scars and fibrosed tissues and neovascularization in fundus images. The proposed algorithm achieves 94.74% sensitivity and 92.11% specificity for screening normal images in the STARE data set from the images with proliferative diabetic retinopathy (PDR). Finally, we present a novel automated system that segments six sub-retinal thickness maps from optical coherence tomography (OCT) image stacks of healthy patients and patients with diabetic macular edema (DME). First, each image in the OCT stack is denoised using a Wiener Deconvolution algorithm that estimates the speckle noise variance using a Fourier-domain based structural error. Next, the denoised images are subjected to an iterative multi-resolution high-pass filtering algorithm that detects seven sub-retinal surfaces in six iterative steps. The thicknesses of each sub-retinal layer for all scans from a particular OCT stack are then combined to generate sub-retinal thickness maps. Using the proposed system the average inner sub-retinal layer thickness in abnormal images is estimated as 275 um (r = 0.92) with an average error of 9.3 um, while the average thickness of the outer segments in abnormal images is estimated as 57.4 um (r = 0.74) with an average error of 3.5 um. Further analysis of the thickness maps from abnormal OCT image stacks demonstrates irregular plateau regions in the inner nuclear layer (INL) and outer nuclear layer (ONL), whose area can be estimated with r = 0.99 by the proposed segmentation system.
  • Thumbnail Image
    Item
    First milk cooperatives assist dairy farmers
    (University of Minnesota Extension, 1915) University of Minnesota Extension
  • Thumbnail Image
    Item
    Hog cholera hits western Minnesota
    (University of Minnesota Extension, 1913) University of Minnesota Extension
  • Thumbnail Image
    Item
    The interaction of structural and inferential elements in characterizing human linguistic communication.
    (2012-01) Lucast, Ellen Irene
    Is human linguistic communication different only in degree from other animal communication, or is it different in kind? If it is different in kind, can this difference best be attributed to one or a small number of core features? If so, what are these features? What role does the code itself play in characterizing human linguistic communication and what role is attributable to its communicative function? To answer these questions, I argue the following: Human linguistic communication is in fact different in kind from other animal communication; its difference can be attributed to two main factors, one coded and one communicative, that lie at the core of the phenomenon of human language; and these two factors are a discrete combinatorial system and the ability to infer others' mental states. I demonstrate that these two factors limit the function of systems which do not display them in ways that are characteristically different from the function of human linguistic communication. This work serves to update existing research on language features by integrating insight from the cognitivist research paradigm that currently prevails in linguistics. It also integrates two traditionally separate areas of inquiry, those of the functioning of the language code itself and of the inferential mechanisms that humans employ when using language for communication, to provide a more comprehensive theory on the nature of human linguistic communication.
  • Thumbnail Image
    Item
    Minnesota farmers become acquainted with soybeans
    (University of Minnesota Extension, 1937) University of Minnesota Extension
  • Thumbnail Image
    Item
    Women pioneers lead way in home economics
    (University of Minnesota Extension, 1911) University of Minnesota Extension