Publications
Paper I Main Author
| Title | Five-dimensional analysis of multi-contrast Jones matrix tomography of posterior eye | ||
| Authors | Udaya Bhaskar, Yoshiaki Yasuno Young-Joo Hong and Masahiro Miura | ||
| Abstract | Pixel clustering algorithm tailored to multi-contrast Jones matrix based optical coherence tomography (MC-JMT) is demonstrated. This algorithm clusters multiple pixels of MC-JMT in a five-dimensional (5-D) feature space which comprises dimensions of lateral space, axial space, logarithmic scattering OCT intensity, squared power of Doppler shift and degree of polarization uniformity. This 5-D clustering provides clusters of pixels, so called as superpixels. The superpixels are utilized as local regions for pixels averaging. The averaging decreases the noise in the measurement as preserving structural details of the sample. A simple decision-tree algorithm is applied to classified superpixels into some tissue types. This classification process successfully segments tissues of a human posterior eye. |
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| Keywords |
Optical coherence tomography; ophthalmology; image processing; clustering; superpixel; polarization
imaging; Doppler imaging; macular disease. |
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Paper II Main Author
| Title | Extraction of Hard Exudates using Functional Link Artificial Neural Networks | ||
| Authors | Udaya Bhaskar and E. Pranay Kumar | ||
| Abstract |
One of the major causes of vision loss is Diabetic Retinopathy (DR). Presence of Hard Exudates
(HE) in retinal images is one of the prominent and most reliable symptoms of Diabetic Retinopathy.
Thus, it is essential to clinically examine for HEs to perform an early diagnosis and monitoring
of DR. In this paper, a classification-based approach using Functional Link Artificial Neural Network (FLANN)
classifier to extract HEs in a retinal fundus image is illustrated. Luminosity Contrast Normalization
pre-processing step was employed. Classification performances were compared between Multi-Layered Perceptron (MLP),
Radial Basis Function (RBF) and FLANN classifiers. Better classification performance was observed for FLANN classifier.
GUI package with Region of Interest (ROI) selection tool was developed.
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| Keywords |
Classifier, Diabetic Retinopathy, Exudates Detection, Functional Link Artificial Neural Network (FLANN), Image Processing, Luminosity Contrast Normalization |
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