Shape models for image segmentation in microscopy

This thesis presents three model driven segmentation algorithms for extracting different types of microscopic objects based on prior shape information of the objects. The development of these algorithms is motivated by the demands of image processing for facilitating the neurological study of memory formation [46] and the investigation of a novel muscle regeneration technique [40]. This thesis has been divided into three parts, and each part proposes a shape model for segmenting the different image objects in different applications. These models are intended to be designed as fully automatic, however in order to achieve better results for the data in different scenarios some parameters in the models require user intervention. The first part presents a novel Gaussian mixture shape modelling algorithm for protein particle detection and analysis in the images delivered from the biological study of memory formation. The biological study investigates the roles of the different types of proteins in the protein synthesis governing the formation of long-term-memory. The investigation is based on the colocalization analysis of different proteins in the living specimen which are stained with different fluorescent markers and are imaged separately by using a confocal microscope. However, some protein particles are clustered in the observed images. In order to detect the candidate particles in each cluster-spot (cluster of particles) that has a merged appearance in the images, the outlined shape modelling algorithm fits the intensity profile of each spot of the protein particles (some are cluster-spots) with a 2-D Gaussian mixture model (GMM). The new GMM approach not only estimates the number of candidate particles in a cluster spot, but also parametrizes the shape characteristics of the particles for the later co-localization analysis. Since the optimization process for the parameters of the GMMs is intractable, a novel error-based split-and-merge expectation-maximization (eSMEM) algorithm (as part of the shape modelling algorithm) is applied to estimate the parameters of the mixture components of the model as well as the number of components. The shape modelling algorithm is completed by adding a preprocessing stage that overcomes scanning-noise and background luminance problems from the acquired image data. During the pre-process, a contrast threshold is defined by the user in order to have a initial rough segmentation of the separate spots in the image. The separated spots are then parameterized by the GMMs automatically. After shape modelling of the particles, the usefulness of the results to the biological analysis is demonstrated by showing how the shape models are used to perform data analysis and co-localization analysis on the protein particles in the images. The second part presents a wavelet-based Bayesian segmentation model to reconstruct the shape the synapses (where the protein synthesis takes place) from a stack of image slices recorded with a confocal microscope. The shape information of the synapses is captured by scanning the synaptic object at multiple different ‘depths’ from top to bottom of the living specimen, and is stores as an image stack of the serially scanned images. The challenges in this work are