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  1. Extraction and reconstruction of retinal vasculature
    Ahmad Fadzil MH, Izhar LI, Venkatachalam PA, Karunakar TV
    J Med Eng Technol, 2007 Nov-Dec;31(6):435-42.
    PMID: 17994417 DOI: 10.1080/03091900601111201
    Information about retinal vasculature morphology is used in grading the severity and progression of diabetic retinopathy. An image analysis system can help ophthalmologists make accurate and efficient diagnoses. This paper presents the development of an image processing algorithm for detecting and reconstructing retinal vasculature. The detection of the vascular structure is achieved by image enhancement using contrast limited adaptive histogram equalization followed by the extraction of the vessels using bottom-hat morphological transformation. For reconstruction of the complete retinal vasculature, a region growing technique based on first-order Gaussian derivative is developed. The technique incorporates both gradient magnitude change and average intensity as the homogeneity criteria that enable the process to adapt to intensity changes and intensity spread over the vasculature region. The reconstruction technique reduces the required number of seeds to near optimal for the region growing process. It also overcomes poor performance of current seed-based methods, especially with low and inconsistent contrast images as normally seen in vasculature regions of fundus images. Simulations of the algorithm on 20 test images from the DRIVE database show that it outperforms many other published methods and achieved an accuracy range (ability to detect both vessel and non-vessel pixels) of 0.91 - 0.95, a sensitivity range (ability to detect vessel pixels) of 0.91 - 0.95 and a specificity range (ability to detect non-vessel pixels) of 0.88 - 0.94.
    Matched MeSH terms: Retinoscopy/methods*
  2. Fulltext Amplitude of Accommodation among Students of a Malaysian Private University as Assessed Using Subjective and Objective Techniques
    Majumder C, Afnan H
    Korean J Ophthalmol, 2020 06;34(3):219-226.
    PMID: 32495530 DOI: 10.3341/kjo.2019.0138
    PURPOSE: The purpose of the study was to establish baseline data for amplitude of accommodation (AA) measured using both subjective and objective techniques in students at a private Malaysian university.

    METHODS: This cross-sectional study was conducted including 34 healthy participants with a mean age of 22.26 ± 1.88 years. AA was measured using dynamic retinoscopy and the push-up, pull-away, modified push-up, and minus-lens techniques.

    RESULTS: The mean AA scores for the push-up, pull-away, minus-lens, and modified push-up techniques and dynamic retinoscopy were 11.38 ± 2.03, 10.35 ± 1.64, 9.24 ± 1.18, 8.26 ± 1.44, and 7.2 ± 1.0 diopters, respectively. No AA measurements showed significant difference among ethnicities (Chinese, Malay, and Indian). This study suggested that AA obtained using push-up (p = 0.005) and pull-away (p = 0.017) methods and dynamic retinoscopy (p = 0.041) were significantly different according to sex. No significant difference was observed in AA for the minus-lens (p = 0.051) and modified push-up (p = 0.216) techniques by sex. A moderately negative correlation was found between AA and age for the push-up (r = -0.434, p = 0.010), pull-away (r = -0.412, p = 0.016), and minus-lens (r = -0.509, p = 0.002) techniques and dynamic retinoscopy (r = -0.497, p = 0.003). A weak negative correlation was found between age and AA measured using a modified push-up technique (r = -0.393, p = 0.022).

    CONCLUSIONS: Mean AA was highest for the push-up technique, followed by the pull-away technique, the minus-lens technique, the modified push up technique, and dynamic retinoscopy. The push-up and pull-away methods and dynamic retinoscopy showed a significant difference in measurement of AA between sexes.

    Matched MeSH terms: Retinoscopy/methods*
  3. Fulltext A new blood vessel extraction technique using edge enhancement and object classification
    Badsha S, Reza AW, Tan KG, Dimyati K
    J Digit Imaging, 2013 Dec;26(6):1107-15.
    PMID: 23515843 DOI: 10.1007/s10278-013-9585-8
    Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.
    Matched MeSH terms: Retinoscopy/methods
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