Detecting circular shapes from areal images using median filter and CHT

One of the challenging topics in image processing is extracting the shapes from noisy backgrounds. There are some methods for doing it from different kinds of noisy backgrounds. In this paper, we are going to introduce another method by using 4 steps to extract circular shapes from impulse noisy backgrounds. First step is applying median filter to disappear "salt and pepper" noise. This step causes edge smoothing. So, as the second step, a laplacian sharpening spatial filter should be applied. It highlights fine details and enhances the blurred edges. Using these two steps sequentially causes noise reduction in an impressive way. Third step is using Canny edge detection for segmenting the image. Its algorithm is talked during the paper. Finally, forth step is applying Circular Hough Transform (CHT) for detecting the circles in image. At the end of paper different use cases of this method is investigated

Quantitative Optical Studies of Oxidative Stress in Rodent Models of Eye and Lung Injuries

Optical imaging techniques have emerged as essential tools for reliable assessment of organ structure, biochemistry, and metabolic function. The recognition of metabolic markers for disease diagnosis has rekindled significant interest in the development of optical methods to measure the metabolism of the organ. The objective of my research was to employ optical imaging tools and to implement signal and image processing techniques capable of quantifying cellular metabolism for the diagnosis of diseases in human organs such as eyes and lungs. To accomplish this goal, three different tools, cryoimager, fluorescent microscope, and optical coherence tomography system were utilized to study the physiological metabolic markers and early structural changes due to injury in vitro, ex vivo, and at cryogenic temperatures. Cryogenic studies of eye injuries in animal models were performed using a fluorescence cryoimager to monitor two endogenous mitochondrial fluorophores, NADH (nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide). The mitochondrial redox ratio (NADH/ FAD), which is correlated with oxidative stress level, is an optical biomarker. The spatial distribution of mitochondrial redox ratio in injured eyes with different durations of the disease was delineated. This spatiotemporal information was helpful to investigate the heterogeneity of the ocular oxidative stress in the eyes during diseases and its association with retinopathy. To study the metabolism of the eye tissue, the retinal layer was targeted, which required high resolution imaging of the eye as well as developing a segmentation algorithm to quantitatively monitor and measure the metabolic redox state of the retina. To achieve a high signal to noise ratio in fluorescence image acquisition, the imaging was performed at cryogenic temperatures, which increased the quantum yield of the intrinsic fluorophores. Microscopy studies of cells were accomplished by using an inverted fluorescence microscope. Fixed slides of the retina tissue as well as exogenous fluorophores in live lung cells were imaged using fluorescent and time-lapse microscopy. Image processing techniques were developed to quantify subtle changes in the morphological parameters of the retinal vasculature network for the early detection of the injury. This implemented image cytometry tool was capable of segmenting vascular cells, and calculating vasculature features including: area, caliber, branch points, fractal dimension, and acellular capillaries, and classifying the healthy and injured retinas. Using time-lapse microscopy, the dynamics of cellular ROS (Reactive Oxygen Species) concentration was quantified and modeled in ROS-mediated lung injuries. A new methodology and an experimental protocol were designed to quantify changes of oxidative stress in different stress conditions and to localize the site of ROS in an uncoupled state of pulmonary artery endothelial cells (PAECs). Ex vivo studies of lung were conducted using a spectral-domain optical coherence tomography (SD-OCT) system and 3D scanned images of the lung were acquired. An image segmentation algorithm was developed to study the dynamics of structural changes in the lung alveoli in real time. Quantifying the structural dynamics provided information to diagnose pulmonary diseases and to evaluate the severity of the lung injury. The implemented software was able to quantify and present the changes in alveoli compliance in lung injury models, including edema. In conclusion, optical instrumentation, combined with signal and image processing techniques, provides quantitative physiological and structural information reflecting disease progression due to oxidative stress. This tool provides a unique capability to identify early points of intervention, which play a vital role in the early detection of eye and lung injuries. The future goal of this research is to translate optical imaging to clinical settings, and to transfer the instruments developed for animal models to the bedside for patient diagnosis

Image segmentation for defect detection on veneer surfaces

Machine vision is widely used in scientific areas and non-wood using industries, but the extreme variability of wood has limited its adoption by forest products industries. However, it is now becoming a key factor in further automation of the forest products industry. As a very important part of machine vision, developing image segmentation algorithms that can be used for wood products is an ambitious undertaking. The focus of this research was to adapt existing and develop some new segmentation algorithms which could be used to detect defects on veneer surfaces. Nine algorithms covering three segmentation technique categories were explored. Three existing edge detection algorithms were modified for use on veneer images, and four existing thresholding algorithms were adapted in both global and local versions. Two new region extraction algorithms were developed specifically for defect detection on veneer surfaces. The performances of these nine algorithms were tested and compared under the combinations of two camera resolutions (5-bit and 8-bit), three color spaces (RGB, Lab, and gray-scale), and seven surface features (clear wood, blue stain, loose knot, pitch pocket, pitch streak, tight knot, and wane). Ten sample images for each of seven surface features on Douglas-fir veneer [Pseudotsuga menziesii] were used. Ten measures were proposed for performance evaluation. A multi-factor factorial ANOVA was used in the performance tests and comparisons. The best combinations of camera resolution and color space for each of the algorithms were determined. The 5-bit and 8-bit camera resolutions were not significantly different for the three edge detection and two region extraction algorithms, but the 8-bit camera resolution was better for all but one of the thresholding algorithms. That exception was the global Otsu thresholding algorithm, for which the 5-bit camera resolution was better. The RGB color space was the best for all algorithms. Overall, the two region extraction algorithms were the best. Under the best combination of factors, those two algorithms provided the highest defect detection accuracies of 91% for pitch streak samples and over 95% for loose knot, tight knot, and pitch pocket samples. These results were accomplished while still providing clear wood accuracies of over 95%. The one performance exception was blue stain, for which no satisfactory algorithm was found

Algorithms offering kinetic analysis of drug induced proteasome inhibition and cell clump formation from time lapsed microscopy

High content screening (HCS) has potential to transform many biological fields, ranging from drug discovery to gene function discovery. HCS with time lapsed microscopy provide valuable insight information about live cells experiments that are usually lost during manual end point experiments. By means of novel bioinformatics algorithms, huge amount of phenotypic data might become available by these techniques which can be used to understand effects of chemical compounds on the cells and profile phenotypically both cell line and chemical compounds. The resultant data can also be compared with other experiments to find out the efficiency and affectivity of the different compounds under same conditions. Recent results also demonstrate that phenotypic profiles can be used to infer specific gene perturbations. In this thesis, novel algorithms for such phenotypic profiling were implemented and demonstrated to be very useful revealing unknown kinetic information regarding two proteasome inhibitors (Bortezomib and CB3) as well as about cell clump formation during cell line growth on honeycomb nanoculture plates. The novel algorithms include specialized solutions both for phase contrast microscopy and fluorescent microscopy and are based on the publicly available cell image processing package Cell Profiler from Broad Institute

Image reconstruction from incomplete information

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Aspects of multi-resolutional foveal images for robot vision

Imperial Users onl