Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics

Current research in the life sciences involves the analysis of such a huge amount of image data that automatization is required. This thesis presents several ways how pattern recognition techniques may contribute to improved tumor diagnostics and to the elucidation of vertebrate embryonic development. Chapter 1 studies an approach for exploiting spatial context for the improved estimation of metabolite concentrations from magnetic resonance spectroscopy imaging (MRSI) data with the aim of more robust tumor detection, and compares against a novel alternative. Chapter 2 describes a software library for training, testing and validating classification algorithms that estimate tumor probability based on MRSI. It allows flexible adaptation towards changed experimental conditions, classifier comparison and quality control without need for expertise in pattern recognition. Chapter 3 studies several models for learning tumor classifiers that allow for the common unreliability of human segmentations. For the first time, models are used for this task that additionally employ the objective image information. Chapter 4 encompasses two contributions to an image analysis pipeline for automatically reconstructing zebrafish embryonic development based on time-resolved microscopy: Two approaches for nucleus segmentation are experimentally compared, and a procedure for tracking nuclei over time is presented and evaluated

Segmentation of phase contrast microscopy images based on multi-scale local Basic Image Features histograms

Phase contrast microscopy (PCM) is routinely used for the inspection of adherent cell cultures in all fields of biology and biomedicine. Key decisions for experimental protocols are often taken by an operator based on typically qualitative observations. However, automated processing and analysis of PCM images remain challenging due to the low contrast between foreground objects (cells) and background as well as various imaging artefacts. We propose a trainable pixel-wise segmentation approach whereby image structures and symmetries are encoded in the form of multi-scale Basic Image Features local histograms, and classification of them is learned by random decision trees. This approach was validated for segmentation of cell versus background, and discrimination between two different cell types. Performance close to that of state-of-the-art specialised algorithms was achieved despite the general nature of the method. The low processing time ( < 4 s per 1280 × 960 pixel images) is suitable for batch processing of experimental data as well as for interactive segmentation applications

A Study on Automated Process for Extracting White Blood Cellular Data from Microscopic Digital Injured Skeletal Muscle Images

Skeletal muscle injury is one of the common injuries caused by high-intensity sports activities, military related works, and natural disasters. In order to discover better therapies, it is important to study muscle regeneration process. Muscle regeneration process tracking is the act of monitoring injured tissue section over time, noting white blood cell behavior and cell-fiber relations. A large number of microscopic images are taken for tracking muscle regeneration process over multiple time instances. Currently, manual approach is widely used to analyze a microscopic image of muscle cross section, which is time consuming, tedious and buggy. Automation of this research methodology is essential to process a big amount of data. The objective of this thesis is to develop a framework to track the regeneration process automatically. The framework includes dynamic thresholding, morphological processing, and feature extraction.Based on the clinical assumptions, the threshold is calculated using standard deviation and mean of probable single cells. After thresholding, six parameters are calculated including average size, cell count, cell area density, cell count on the basis of size, the number of cytoplasmic and membrane cells as well as the average distance between cellular objects. All these parameters are estimated over the time, which helped to note the pattern of change in leukocytes (White blood cells) presence. Based on these results, a clear pattern of leukocyte movement is observed. Our future work includes deriving the mathematical equations using regression model on the data set of increased time points