Image Processing and Analysis for Measuring Ocular Refraction Errors / "Processamento e análise de imagens para medição de vícios de refração ocular"

AUTOR(ES)
DATA DE PUBLICAÇÃO

2003

RESUMO

This work presents a computational system that uses Machine Learning (ML) techniques to assist in ophthalmological diagnosis. The system developed produces objective and automatic measures of ocular refraction errors, namely astigmatism, hypermetropia and myopia from functional images of the human eye acquired with a technique known as Hartmann-Shack (HS), or Shack-Hartmann (SH). Image processing techniques are applied to these images in order to remove noise and extract the regions of interest. The Gabor wavelet transform technique is applied to extract feature vectors from the images, which are then input to ML techniques that output a diagnosis of the refractive errors in the imaged eye globe. Results indicate that the proposed approach creates interesting possibilities for the interpretation of HS images, so that in the future other types of ocular diseases may be detected and measured from the same images. In addition to implementing a novel approach for measuring ocular refraction errors and introducing ML techniques for analyzing ophthalmological images, this work investigates the use of Artificial Neural Networks and Support Vector Machines (SVMs) for tasks in Image Understanding. The description of the process adopted for developing this system can help in critically verifying the suitability and limitations of such techniques for solving Image Understanding tasks in "real world" problems.

ASSUNTO(S)

machine learning entendimento de imagens intelligent systems optometry auto-refractor hartmann-shack engenharia biomédica image understanding aprendizado de máquina support vector machines (svm) hartmann-shack sistemas inteligentes auto-refratores artificial neural networks refractive errors máquinas de vetores suporte (svm) biomedical engineering redes neurais artificiais vícios refrativos optometria

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