Study and Analysis of Fluid Filled Abnormalities in Retina Using OCT Images

Visual impairment is one of the most regularly happening infections in human. The reason being variation from the normal in the different layers of retina because of strange measure of liquid either abundance aggregation or shortage. This paper targets recognizing and assessing the different abnormalities that could be earlier stages to visual deficiency. The proposed target is achieved by means of implementation using Digital Image Processing Technique, starting from preprocessing to classification at various stages. Not restricting to binary classification as normal or abnormal, the proposed system also extends its capacity to classify the input image as Cystoid Macular Edema (CME), Choroidal Neo Vascular Membrane (CNVM), Macular Hole (MH) and normal images. The preprocessing methodology implemented filters to remove the speckle noises which are most common in ultrasound-based imaging system. Random forest classifier was utilized for classifying the input features and also seems to be promising on par with the various existing methodologies

Development of an OCT Simulator using a KTN Wavelength Swept Light Source

Optical Coherence Tomography (OCT) is used to acquire tomographic images in the retina of an eye. This is an imaging device that uses near-infrared interference, and it enables early detection of glaucoma and diabetic retinopathy. However, OCT images include a lot of noise such as speckle noise, and it is necessary to develop denoising methods. In this study, we developed a simulator to generate OCT images using a KTN wavelength swept light source, providing a number of OCT images used for a deep learning method to remove these noise

Contourlet Domain Image Modeling and its Applications in Watermarking and Denoising

Statistical image modeling in sparse domain has recently attracted a great deal of research interest. Contourlet transform as a two-dimensional transform with multiscale and multi-directional properties is known to effectively capture the smooth contours and geometrical structures in images. The objective of this thesis is to study the statistical properties of the contourlet coefficients of images and develop statistically-based image denoising and watermarking schemes. Through an experimental investigation, it is first established that the distributions of the contourlet subband coefficients of natural images are significantly non-Gaussian with heavy-tails and they can be best described by the heavy-tailed statistical distributions, such as the alpha-stable family of distributions. It is shown that the univariate members of this family are capable of accurately fitting the marginal distributions of the empirical data and that the bivariate members can accurately characterize the inter-scale dependencies of the contourlet coefficients of an image. Based on the modeling results, a new method in image denoising in the contourlet domain is proposed. The Bayesian maximum a posteriori and minimum mean absolute error estimators are developed to determine the noise-free contourlet coefficients of grayscale and color images. Extensive experiments are conducted using a wide variety of images from a number of databases to evaluate the performance of the proposed image denoising scheme and to compare it with that of other existing schemes. It is shown that the proposed denoising scheme based on the alpha-stable distributions outperforms these other methods in terms of the peak signal-to-noise ratio and mean structural similarity index, as well as in terms of visual quality of the denoised images. The alpha-stable model is also used in developing new multiplicative watermark schemes for grayscale and color images. Closed-form expressions are derived for the log-likelihood-based multiplicative watermark detection algorithm for grayscale images using the univariate and bivariate Cauchy members of the alpha-stable family. A multiplicative multichannel watermark detector is also designed for color images using the multivariate Cauchy distribution. Simulation results demonstrate not only the effectiveness of the proposed image watermarking schemes in terms of the invisibility of the watermark, but also the superiority of the watermark detectors in providing detection rates higher than that of the state-of-the-art schemes even for the watermarked images undergone various kinds of attacks