Bayesian multi-target tracking: application to total internal reflection fluorescence microscopy

This thesis focuses on the problem of automated tracking of tiny cellular and sub-cellular structures, known as particles, in the sequences acquired from total internal reflection fluorescence microscopy (TIRFM) imaging technique. Our primary biological motivation is to develop an automated system for tracking the sub-cellular structures involving exocytosis (an intracellular mechanism) which is helpful for studying the possible causes of the defects in diseases such as diabetes and obesity. However, all methods proposed in this thesis are generalized to be applicable for a wide range of particle tracking applications. A reliable multi-particle tracking method should be capable of tracking numerous similar objects in the presence of high levels of noise, high target density and complex motions and interactions. In this thesis, we choose the Bayesian filtering framework as our main approach to deal with this problem. We focus on the approaches that work based on detections. Therefore, in this thesis, we first propose a method that robustly detects the particles in the noisy TIRFM sequences with inhomogeneous and time-varying background. In order to evaluate our detection and tracking methods on the sequences with known and reliable ground truth, we also present a framework for generating realistic synthetic TIRFM data. To propose a reliable multi-particle tracking method for TIRFM sequences, we suggest a framework by combining two robust Bayesian filters, the interacting multiple model and joint probabilistic data association (IMM-JPDA) filters. The performance of our particle tracking method is compared against those of several popular and state-of-the art particle tracking approaches on both synthetic and real sequences. Although our approach performs well in tracking particles, it can be very computationally demanding for the applications with dense targets with poor detections. To propose a computationally cheap, but reliable, multi-particle tracking method, we investigate the performance of a recent multi-target Bayesian filter based on random finite theory, the probability hypothesis density (PHD) filter, on our application. To this end, we propose a general framework for tracking particles using this filter. Moreover, we assess the performance of our proposed PHD filter on both synthetic and real sequences with high level of noise and particle density. We compare its results from both aspects of accuracy and processing time against our IMM-JPDA filter. Finally, we suggest a framework for tracking particles in a challenging problem where the noise characteristic and the background intensity of sequences change during the acquisition process which make detection profile and clutter rate time-variant. To deal with this, we propose a bootstrap filter using another type of the random finite set based Bayesian filters, the cardinalized PHD (CPHD) filter, composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability while the tracker estimates the state of the targets. We evaluate the performance of our bootstrap on both synthetic and real sequences under these time-varying conditions. Moreover, its performance is compared against those of our other particle trackers as well as the state-of-the art particle tracking approaches

A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences

Abstract. Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a recent Bayesian tracking filter that is well-suited for this task. However, the existing recursion equations for this filter do not consider a state-dependent transition probability matrix. As required in many biological applications, we propose a new closed-form recursion that incorporates this assumption and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters

Retinal vessel segmentation using a multi-scale medialness function

Recently, automated segmentation of retinal vessels in optic fundus images has been an important focus of much research. In this paper, we propose a multi-scale method to segment retinal vessels based on a weighted two-dimensional (2D) medialness function. The results of the medialness function are first multiplied by the eigenvalues of the Hessian matrix. Next, centerlines of vessels are extracted using noise reduction and reconnection procedures. Finally, vessel radii are estimated and retinal vessels are segmented. The proposed method is evaluated and compared with several recent methods using images from the DRIVE and STARE databases

A new approach for spot detection in total internal reflection fluorescence microscopy

Biological images acquired from fluorescence microscopy-based imaging techniques, such as total internal reflection fluorescence microscopy, generally contain hundreds of subcellular components, appearing in the images as bright spots. Therefore, the first step of analysis of these images usually involves the detection of these bright spots. In this paper, we propose a new approach for spot detection using the h-dome transform along with an adaptive mask obtained from regional information. Moreover, local gradient information is used in order to distinguish the spots from other structures. To evaluate the performance of our algorithm, we test it on synthetic images and also real TIRFM images and compare our results with those of two recent methods

Application of the IMM-JPDA filter to multiple target tracking in total internal reflection fluorescence microscopy images

We propose a multi-target tracking method using an Interacting Multiple Model Joint Probabilistic Data Association (IMM-JPDA) filter for tracking vesicles in total internal reflection fluorescence microscopy (TIRFM) sequences. We enhance the accuracy an

Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences

© 2015 IEEE. Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, complex motion patterns and intricate interactions. In this paper, we propose a framework for tracking these structures based on the random finite set Bayesian filtering framework. We focus on challenging biological applications where image characteristics such as noise and background intensity change during the acquisition process. Under these conditions, detection methods usually fail to detect all particles and are often followed by missed detections and many spurious measurements with unknown and time-varying rates. To deal with this, we propose a bootstrap filter composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability of the targets, while the tracker estimates the state of the targets. Our results show that the proposed approach can outperform state-of-the-art particle trackers on both synthetic and real data in this regime