Minimum S-Excess Graph for Segmenting and Tracking Multiple Borders with HMM

Numer Methods Biomed Eng

2019

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In this paper, we present a novel image segmentation technique, based on hidden Markov model (HMM), which we then apply to simultaneously segment interior and exterior walls of fluorescent confocal images of lymphatic vessels. Our proposed method achieves this by tracking hidden states, which are used to indicate the locations of both the inner and outer wall borders throughout the sequence of images. We parameterize these vessel borders using radial basis functions (RBFs), thus enabling us to minimize the number of points we need to track as we progress through multiple layers and therefore reduce computational complexity. Information about each border is detected using patch‐wise convolutional neural networks (CNN). We use the softmax function to infer the emission probability and use a proposed new training algorithm based on s‐excess optimization to learn the transition probability. We also introduce a new optimization method to determine the optimum sequence of the hidden states. Thus, we transform the segmentation problem into one that minimizes an s‐excess graph cut, where each hidden state is represented as a graph node and the weight of these nodes are defined by their emission probabilities. The transition probabilities are used to define relationships between neighboring nodes in the constructed graph. We compare our proposed method to the Viterbi and Baum–Welch algorithms. Both qualitative and quantitative analysis show superior performance of the proposed methods.

Section: Introductionsupporting

confidence: 69%

supporting

confidence: 73%

Coupled s‐excess HMM for vessel border tracking and segmentation

Essa

Jones

Numer Methods Biomed Eng

2019

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“…In preprocessing, for each eye, the outline of the choroidal region was manually labelled on every tenth slice, hence meaning the dataset consisted of over 3,800 labelled slices. Automatic image segmentation has been shown to work in medical examples [19], [20], [21] but we chose manual segmentation to ensure accuracy and consistency. Fig.…”

Section: Resultsmentioning

confidence: 99%

Learning feature extractors for AMD classification in OCT using convolutional neural networks

Ravenscroft

Deng

2017 25th European Signal Processing Conference (EUSIPCO)

et al. 2017

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Abstract-In this paper, we propose a two-step textural feature extraction method, which utilizes the feature learning ability of Convolutional Neural Networks (CNN) to extract a set of low level primitive filter kernels, and then generalizes the discriminative power by forming a histogram based descriptor. The proposed method is applied to a practical medical diagnosis problem of classifying different stages of Age-Related Macular Degeneration (AMD) using a dataset comprising long-wavelength Optical Coherence Tomography (OCT) images of the choroid. The experimental results show that the proposed method extracts more discriminative features than the features learnt through CNN only. It also suggests the feasibility of classifying different AMD stages using the textural information of the choroid region.

“…To simultaneously extract the inner and outer walls of the lymphatic vessel in confocal microscopy images, Essa et al developed an approach based on the hidden Markov model (HMM). The segmentation problem is transformed into minimizing the cost of an s‐excess graph, where each graph node corresponds to a hidden state and its weight is defined by the emission probability inferred from two separate Gaussian probability distributions for the vessel borders and background.…”

Section: Applications Of Energy Minimizationmentioning

confidence: 99%

“…Examples of graph‐based medical image segmentation. (a) Media‐adventitia border detection in an intravascular ultrasound image of coronary artery (green: ground truth; red: Essa's method ), and (b) 3D visualization of detected lymphatic vessel walls (yellow: inner wall; red: outer wall).…”

Section: Applications Of Energy Minimizationmentioning

confidence: 99%

Energy minimization in medical image analysis: Methodologies and applications

Zhang

Numer Methods Biomed Eng

2015

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Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree-reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, for example, image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well.