Support vector machines in spectral data classification

Työssä käydään läpi tukivektorikoneiden teoreettista pohjaa sekä tutkitaan eri parametrien vaikutusta spektridatan luokitteluun.

Tympanometry and Spectral Gradient Acoustic Reflectometry in the Diagnosis of Otitis Media in Young Children

Acute otitis media (AOM) is the most prevalent bacterial infection among children. Tympanometry and spectral gradient acoustic reflectometry (SG-AR) are adjunctive diagnostic tools to pneumatic otoscopy. The aim was to investigate the diagnostic accuracy and success rates of tympanometry and SG-AR performed by physicians and nurses. The study populations comprised 515 (I-II), 281 (III), and 156 (IV) outpatients (6-35 months). Physicians performed 4246 tympanometric (I) and SG-AR (II) examinations. Nurses performed 1782 (III) and 753 (IV) examinations at symptomatic and asymptomatic visits, respectively. Pneumatic otoscopy by the physician was the diagnostic standard. The accuracy of test results by physicians or nurses (I-IV) and the proportion of visits with accurate exclusive test results from both ears (III-IV) were analyzed. Type B tympanogram and SG-AR level 5 (<49˚) predicted middle ear effusion (MEE). At asymptomatic visits, type A and C1 tympanograms (peak pressure > -200 daPa) and SG-AR level 1 (>95˚) indicated healthy middle ear. Negative predictive values of type A and C1 tympanograms by nurses in excluding AOM at symptomatic and MEE at asymptomatic visits were 94% and 95%, respectively. Nurses obtained type A or C1 tympanogram from both ears at 94/459 (20%) and 81/196 (41%) of symptomatic and asymptomatic visits, respectively. SG-AR level 1 was rarely obtained from both ears. Type A and C1 tympanograms were accurate in excluding AOM at symptomatic and MEE at asymptomatic visits. However, nurses obtained these tympanograms from both ears only at one fifth of symptomatic visits and less than half of asymptomatic visits.

Spectral characteristics of bacteriorhodopsin water solution

The object of the study is bacteriorhodopsin. This light-sensitive protein have been selected as perspective substance for optical and optoelectronic applications. Bacteriorhodopsin carries out pumping protons through the cell membrane. Biomolecule converts light into an electric signal when sandwiched between electrodes. These properties were utilized in this research to implement photosensors on the basis of BR layers. These properties were utilized in this research to the bR water solution. According to the absorption spectra and using Kramers – Kronig relation the extinction coefficient has been calculated, as well as the related change of the refractive index value.

Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels

This work investigates theoretical properties of symmetric and anti-symmetric kernels. First chapters give an overview of the theory of kernels used in supervised machine learning. Central focus is on the regularized least squares algorithm, which is motivated as a problem of function reconstruction through an abstract inverse problem. Brief review of reproducing kernel Hilbert spaces shows how kernels define an implicit hypothesis space with multiple equivalent characterizations and how this space may be modified by incorporating prior knowledge. Mathematical results of the abstract inverse problem, in particular spectral properties, pseudoinverse and regularization are recollected and then specialized to kernels. Symmetric and anti-symmetric kernels are applied in relation learning problems which incorporate prior knowledge that the relation is symmetric or anti-symmetric, respectively. Theoretical properties of these kernels are proved in a draft this thesis is based on and comprehensively referenced here. These proofs show that these kernels can be guaranteed to learn only symmetric or anti-symmetric relations, and they can learn any relations relative to the original kernel modified to learn only symmetric or anti-symmetric parts. Further results prove spectral properties of these kernels, central result being a simple inequality for the the trace of the estimator, also called the effective dimension. This quantity is used in learning bounds to guarantee smaller variance.

Spectral retinal image processing and analysis for ophthalmology

Diabetic retinopathy, age-related macular degeneration and glaucoma are the leading causes of blindness worldwide. Automatic methods for diagnosis exist, but their performance is limited by the quality of the data. Spectral retinal images provide a significantly better representation of the colour information than common grayscale or red-green-blue retinal imaging, having the potential to improve the performance of automatic diagnosis methods. This work studies the image processing techniques required for composing spectral retinal images with accurate reflection spectra, including wavelength channel image registration, spectral and spatial calibration, illumination correction, and the estimation of depth information from image disparities. The composition of a spectral retinal image database of patients with diabetic retinopathy is described. The database includes gold standards for a number of pathologies and retinal structures, marked by two expert ophthalmologists. The diagnostic applications of the reflectance spectra are studied using supervised classifiers for lesion detection. In addition, inversion of a model of light transport is used to estimate histological parameters from the reflectance spectra. Experimental results suggest that the methods for composing, calibrating and postprocessing spectral images presented in this work can be used to improve the quality of the spectral data. The experiments on the direct and indirect use of the data show the diagnostic potential of spectral retinal data over standard retinal images. The use of spectral data could improve automatic and semi-automated diagnostics for the screening of retinal diseases, for the quantitative detection of retinal changes for follow-up, clinically relevant end-points for clinical studies and development of new therapeutic modalities.

Spectral Retinal Images Reconstruction

While red-green-blue (RGB) image of retina has quite limited information, retinal multispectral images provide both spatial and spectral information which could enhance the capability of exploring the eye-related problems in their early stages. In this thesis, two learning-based algorithms for reconstructing of spectral retinal images from the RGB images are developed by a two-step manner. First, related previous techniques are reviewed and studied. Then, the most suitable methods are enhanced and combined to have new algorithms for the reconstruction of spectral retinal images. The proposed approaches are based on radial basis function network to learn a mapping from tristimulus colour space to multi-spectral space. The resemblance level of reproduced spectral images and original images is estimated using spectral distance metrics spectral angle mapper, spectral correlation mapper, and spectral information divergence, which show a promising result from the suggested algorithms.