Ahmed Gawish

Automatic Detection of the Ice Edge in SAR Imagery Using Curvelet Transform and Active Contour

A novel method based on the curvelet transform and active contour method to automatically detect the ice edge in Synthetic Aperture Radar (SAR) imagery is proposed. The method utilizes the location of high curvelet coefficients to determine regions in the image likely to contain the ice edge. Using an ice edge from passive microwave sea ice concentration for initialization, these regions are then joined using the active contour method to obtain the final ice edge. The method is evaluated on four dual polarization SAR scenes of the Labrador sea. Through comparison of the ice edge with that from image analysis charts, it is demonstrated that the proposed method can detect the ice edge effectively in SAR images. This is particularly relevant when the marginal ice zone is diffuse or the ice is thin, and using the definition of ice edge from the passive microwave ice concentration would underestimate the ice edge location. It is expected that the method may be useful for operations in marginal ice zones, such as offshore drilling, where a high resolution estimate of the ice edge location is required. It could also be useful as a first guess for an ice analyst, or for the assimilation of SAR data.

In-vivo imaging of keratoconic corneas using high-speed high-resolution swept-source OCT

Keratoconus (KC) is a progressive degenerative corneal disease that can lead to a strong deformation of the cornea and loss of clarity, causing distorted or blurred vision. Surgical treatment for severe cases requires precise evaluation of the corneal curvature, thickness, layer structure, and clarity. Current clinical instruments for assessing the corneal shape cannot resolve the internal structure, and high-resolution microscopy techniques are limited to a small field of view. We have implemented a swept-source OCT (SS-OCT) system that enables high-speed imaging (100 kA-scans/s) of the entire cornea and provides ~5.1μm axial resolution in corneal tissue. With an imaging range of 5.6 mm (in air), we can cover the full length from the cornea’s apex to the anterior surface of the lens. We have acquired volumetric corneal images from human subjects with different stages of KC and from subjects who underwent surgery or cross-linking therapy. We developed an automatic algorithm for segmenting the outer and inner surfaces of the cornea in the images which will enable precise measurement of the corneal curvature and thickness. This makes SS-OCT an ideal instrument for comprehensive examination of keratoconic corneas.

External forces for active contours using the undecimated wavelet transform

A limitation of active contours models (both parametric and geometric) is their sensitivity to noise. Many solutions to noise sensitivity have been proposed in the literature, with the current state-of-the-art based on image blurring and multiresolution processing. However a significant drawback of both approaches is the side effect of edge delocalization. In this paper, gradient information extracted from all resolutions of the undecimated wavelet transform is used to build the external force map for the active contour. The new map accurately drives the active contour and improves edge localization. The proposed method builds on both Gradient Vector Flow and Vector Field Convolution active contours. Comparisons to classical and state-of-the-art methods show a dramatic improvement in active contour convergence for all levels of noise.

Impact of Training Images on Radiometric Compensation

The increasing availability of both high-resolution projectors and imperfect displays make radiometric correction an essential component in all modern projection systems. Particularly, projecting in casual locations, such as classrooms, open areas and homes, calls for the development of radiometric correction techniques that are fully automatic and deal with display imperfections in real-time. This paper reviews the current radiometric compensation algorithms and discusses the influence of different training images on their performance.

Robust Non-saliency Guided Watermarking

A non-saliency guided watermarking approach is introduced where the watermark is embedded in natural images based on three measures, namely, non-saliency, heterogeneity and brightness. The three measures aim to exploit the main characteristics of the human visual system (HVS) for the purpose of embedding watermarks in images. The proposed method has the following advantages: 1) increasing the imperceptibility of the watermark through modeling HVS, 2) robustness to different attacks through embedding the data into non-salient areas with strong features and 3)increasing the watermarking capacity of labeling image pixels as appropriate or inappropriate candidates by using the continuous non-saliencyheterogeneity-brightness spectrum as opposed to binary labelingschemes as suggested by other methods. A variety of experimentsdemonstrate the superiority of the proposed method over existing state-of-the-art methods in terms of robustness and imperceptibility.

Undecimated hierarchical active contours for oct image segmentation

A limitation of Optical Coherence Tomography (OCT) image segmentation is the poor signal-to-noise ratio of the imaging process, particularly because images are sampled quickly, at high resolutions, and in-vivo. Furthermore, speckle noise is generated by the reflections of the OCT LASER. Because OCT is widely used in imaging the cornea, retina, and skin, OCT layer segmentation is of key interest in all applications. In this paper, a multi-resolution parametric active contour is used for OCT segmentation. The proposed method uses an undecimated wavelet transform to obtain scale-dependent noise reduction, while the active contour is initialized with a generalized Hough transform. Experimental results show that the proposed method outperforms classical as well as state-of-the-art methods and segments OCT images with high level of accuracy.