Mathematical Morphology for Quantification in Biological & Medical Image Analysis

Mathematical morphology is an established field of image processing first introduced as an application of set and lattice theories. Originally used to characterise particle distributions, mathematical morphology has gone on to be a core tool required for such important analysis methods as skeletonisation and the watershed transform. In this thesis, I introduce a selection of new image analysis techniques based on mathematical morphology. Utilising assumptions of shape, I propose a new approach for the enhancement of vessel-like objects in images: the bowler-hat transform. Built upon morphological operations, this approach is successful at challenges such as junctions and robust against noise. The bowler-hat transform is shown to give better results than competitor methods on challenging data such as retinal/fundus imagery. Building further on morphological operations, I introduce two novel methods for particle and blob detection. The first of which is developed in the context of colocalisation, a standard biological assay, and the second, which is based on Hilbert-Edge Detection And Ranging (HEDAR), with regard to nuclei detection and counting in fluorescent microscopy. These methods are shown to produce accurate and informative results for sub-pixel and supra-pixel object counting in complex and noisy biological scenarios. I propose a new approach for the automated extraction and measurement of object thickness for intricate and complicated vessels, such as brain vascular in medical images. This pipeline depends on two key technologies: semi-automated segmentation by advanced level-set methods and automatic thickness calculation based on morphological operations. This approach is validated and results demonstrating the broad range of challenges posed by these images and the possible limitations of this pipeline are shown. This thesis represents a significant contribution to the field of image processing using mathematical morphology and the methods within are transferable to a range of complex challenges present across biomedical image analysis

SPHEROID DETECTION IN 2D IMAGES USING CIRCULAR HOUGH TRANSFORM

Three-dimensional endothelial cell sprouting assay (3D-ECSA) exhibits differentiation of endothelial cells into sprouting structures inside a 3D matrix of collagen I. It is a screening tool to study endothelial cell behavior and identification of angiogenesis inhibitors. The shape and size of an EC spheroid (aggregation of ~ 750 cells) is important with respect to its growth performance in presence of angiogenic stimulators. Apparently, tubules formed on malformed spheroids lack homogeneity in terms of density and length. This requires segregation of well formed spheroids from malformed ones to obtain better performance metrics. We aim to develop and validate an automated imaging software analysis tool, as a part of a High-content High throughput screening (HC-HTS) assay platform, to exploit 3D-ECSA as a differential HTS assay. We present a solution using Circular Hough Transform to detect a nearly perfect spheroid as per its circular shape in a 2D image. This successfully enables us to differentiate and separate good spheroids from the malformed ones using automated test bench

Towards a Strawberry Harvest Prediction System Using Computer Vision and Pattern Recognition

Farmers require advance notice when a harvest is approaching, so they can allocate resources and hire workers as efficiently as possible. Existing methods are subjective and labor intensive, and require the expertise of a professional forecaster. Cal Poly’s EE department has been collaborating with the Cal Poly Strawberry Center to investigate the potential in using digital imaging processing to predict harvests more reliably. This paper shows the progress of that ongoing project, as well as what aspects could still be improved. Three main blocks comprise this system: data acquisition, which obtains and catalogues images of the strawberry plants; computer vision, which extracts information from the images and constructs a time-series model of the field as a whole; and prediction, which uses the field’s history to guess when the most likely harvest window will be. The best method of data acquisition is determined through a decision matrix to be a small autonomous rover. Several challenges specific to images captured via drone, such as fisheye distortion and dirt masking, are examined and mitigated. Using thresholding, the nRGB color space is shown to be the most promising for image segmentation of red strawberries. Data from field 25 at the Cal Poly Strawberry Center is tabulated, analyzed, and compared against industry trends across California. Ultimately, this work serves as a strong benchmark towards a full strawberry yield prediction system

Automated processing of oceanic bubble images for measuring bubble size distributions underneath breaking waves

Accurate in situ measurements of oceanic bubble size distributions beneath breaking waves are needed for a better understanding of air–sea gas transfer and aerosol production processes. To achieve this goal, a novel high-resolution optical instrument for imaging oceanic bubbles was designed and built in 2013 for the High Wind Gas Exchange Study (HiWinGS) campaign in the North Atlantic Ocean. The instrument is able to operate autonomously and can continuously capture high-resolution images at 15 frames per second over an 8-h deployment. The large number of images means that it is essential to use an automated processing algorithm to process these images. This paper describes an automated algorithm for processing oceanic images based on a robust feature extraction technique. The main advantages of this robust algorithm are it is significantly less sensitive to the noise and insusceptible to the background changes in illumination, can extract circular bubbles as small as one pixel (approximately 20 μm) in radius accurately, has low computing time (approximately 5 seconds per image), and is simple to implement. The algorithm was successfully used to analyze a large number of images (850 000 images) from deployment in the North Atlantic Ocean as part of the HiWinGS campaign in 2013

Computer Aided Diagnosis for Screening the Shape and Size of Leukocyte Cell Nucleus based on Morphological Image

Hematology tests are examinations that aim to know the state of blood and its components, one of which is leukocytes. Hematologic examinations such as the number and morphology of blood generally still done manually, especially by a specialist pathologist. Despite the fact that today there is equipment that can identify morphological automatically, but for developing countries like Indonesia, it can only be done in the capital city. Low accuracy due to the differences identified either by doctors or laboratory staff, makes a great reason to use computer assistance, especially with the rapid technological developments at this time. In this paper, we will emphasize our experiment to screen leucocyte cell nucleus by identifying the contours of the cell nucleus, diameter, circumference and area of these cells based on digital image processing techniques, especially using the morphological image. The results obtained are promising for further development in the development of computer-aided diagnosis for identification of leukocytes based on a simple and inexpensive equipment

Automated image-based quality control of molecularly imprinted polymer films

We present results of applying a feature extraction process to images of coatings of molecularly imprinted polymers (MIPs) coatings on glass substrates for defect detec- tion. Geometric features such as MIP side lengths, aspect ratio, internal angles, edge regularity, and edge strength are obtained by using Hough transforms, and Canny edge detection. A Self Organizing Map (SOM) is used for classification of texture of MIP surfaces. The SOM is trained on a data set comprised of images of manufactured MIPs. The raw images are first processed using Hough transforms and Canny edge detection to extract just the MIP-coated portion of the surface, allowing for surface area estimation and reduction of training set size. The training data set is comprised of 20-dimensional feature vectors, each of which is calculated from a single section of a gray scale image of a MIP. Haralick textures are among the quantifiers used as feature vector components. The training data is then processed using principal component analysis to reduce the number of dimensions of the data set. After training, the SOM is capable of classifying texture, including defects

The ArgoNeuT Detector in the NuMI Low-Energy beam line at Fermilab

The ArgoNeuT liquid argon time projection chamber has collected thousands of neutrino and antineutrino events during an extended run period in the NuMI beam-line at Fermilab. This paper focuses on the main aspects of the detector layout and related technical features, including the cryogenic equipment, time projection chamber, read-out electronics, and off-line data treatment. The detector commissioning phase, physics run, and first neutrino event displays are also reported. The characterization of the main working parameters of the detector during data-taking, the ionization electron drift velocity and lifetime in liquid argon, as obtained from through-going muon data complete the present report.Comment: 43 pages, 27 figures, 5 tables - update referenc