@article {278, title = {Intra-retinal layer segmentation in optical coherence tomography images}, journal = {Optics Express}, volume = {17}, year = {2009}, pages = {23719{\textendash}23728}, abstract = {

Retinal layer thickness, evaluated as a function of spatial position from optical coherence tomography (OCT) images is an important diagnostics marker for many retinal diseases. However, due to factors such as speckle noise, low image contrast, irregularly shaped morphological features such as retinal detachments, macular holes, and drusen, accurate segmentation of individual retinal layers is difficult. To address this issue, a computer method for retinal layer segmentation from OCT images is presented. An efficient two-step kernel-based optimization scheme is employed to first identify the approximate locations of the individual layers, which are then refined to obtain accurate segmentation results for the individual layers. The performance of the algorithm was tested on a set of retinal images acquired in-vivo from healthy and diseased rodent models with a high speed, high resolution OCT system. Experimental results show that the proposed approach provides accurate segmentation for OCT images affected by speckle noise, even in sub-optimal conditions of low image contrast and presence of irregularly shaped structural features in the OCT images.

}, keywords = {algorithms and filters, image processing, image recognition, optical coherence tomography, Optical diagnostics for medicine}, doi = {http://dx.doi.org/10.1364/OE.17.023719}, url = {http://www.opticsexpress.org/abstract.cfm?URI=oe-17-26-23719}, author = {K Bizheva and A Mishra and A Wong and D A. Clausi} } @inproceedings {368, title = {Robust invariant descriptor for symbol-based image recognition and retrieval}, booktitle = {1st IEEE International Conference on Semantic Computing}, year = {2007}, abstract = {

This paper presents a robust invariant descriptor for symbol-based image recognition and retrieval. A modified Hough-based Transform is used to extract parameter space information (i.e., position data and angular data) from a symbol image to derive an invariant descriptor. The proposed descriptor provides a compact representation of a symbol image that can be evaluated efficiently. The extracted descriptor is highly robust against geometric transformations such as translation, rotation, reflection, and scaling, and image degradation. A series of experiments were conducted using a set of architectural and engineering symbols subjected to geometric transformations and image degradation. The experimental results clearly show that the proposed descriptor can be used effectively for symbol recognition and retrieval.

}, keywords = {content-based retrieval, Feature Extraction, geometric transformation, Hough transforms, Hough-based transform, image degradation, image recognition, image representation, image retrieval, robust invariant descriptor, symbol-based image recognition}, doi = {http://dx.doi.org/10.1109/ICSC.2007.24}, author = {A Wong and W Bishop} } @inproceedings {510, title = {Automated analysis and detection of cracks in underground scanned pipes}, booktitle = {International Conference on Image Processing}, year = {1999}, abstract = {

Closed circuit television (CCTV) surveys are used widely in North America to assess the structural integrity of underground pipes. The video images are examined visually and classified into grades according to degrees of damage. The human eye is extremely effective at recognition and classification, but it is not suitable for assessing pipe defects in thousands of miles of pipeline images due to fatigue, subjectivity and cost. This paper presents ongoing research into the automatic assessment of the structural condition of underground pipes for the purpose of preventive maintenance by municipalities

}, keywords = {CCTV, civil engineering computing, closed circuit television, crack analysis, Crack detection, Feature Extraction, image classification, image recognition, maintenance engineering, pipe defects, preventive maintenance, underground pipe structural integrity, underground scanned pipes, video images, video signal processing}, doi = {http://dx.doi.org/10.1109/ICIP.1999.819622}, author = {P Fieguth and S K. Sinha} }