Shape Prior Model for Media-Adventitia Border Segmentation in IVUS Using Graph Cut

2017 25th European Signal Processing Conference (EUSIPCO)

et al. 2017

Self Cite

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.

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

Self Cite

“…There have been many different approaches to the problem of segmenting IVUS images, for example . These can be broadly categorized into fully automatic methods or methods that allow user interactions.…”

Section: Resultsmentioning

confidence: 99%

“…Incorporating user prior knowledge into segmentation hence is often necessary and has been shown to be an effective approach. For instance, Essa et al , incorporated a shape prior to graph‐cut construction to regularize segmentation of media–adventitia border. However, these approaches generally require significant amount of training data, and model re‐training is often necessary in order to adapt to new data sets.…”

Section: Resultsmentioning

confidence: 99%

“…Because of the nature of these artefacts, automatic methods employed in these applications require a significant amount of pre‐processing : a large data set for training set or a method to remove the artefacts . Our proposed method, by giving the user control to help the process, removes the need for extensive pre‐processing of our large data sets for training.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining region‐based and imprecise boundary‐based cues for interactive medical image segmentation

Jones

Numer Methods Biomed Eng

Essa

2014

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In this paper, we present an approach combining both region selection and user point selection for user-assisted segmentation as either an enclosed object or an open curve, investigate the method of image segmentation in specific medical applications (user-assisted segmentation of the media-adventitia border in intravascular ultrasound images, and lumen border in optical coherence tomography images), and then demonstrate the method with generic images to show how it could be utilized in other types of medical image and is not limited to the applications described. The proposed method combines point-based soft constraint on object boundary and stroke-based regional constraint. The user points act as attraction points and are treated as soft constraints rather than hard constraints that the segmented boundary has to pass through. The user can also use strokes to specify region of interest. The probabilities of region of interest for each pixel are then calculated, and their discontinuity is used to indicate object boundary. The combinations of different types of user constraints and image features allow flexible and robust segmentation, which is formulated as an energy minimization problem on a multilayered graph and is solved using a shortest path search algorithm. We show that this combinatorial approach allows efficient and effective interactive segmentation, which can be used with both open and closed curves to segment a variety of images in different ways. The proposed method is demonstrated in the two medical applications, that is, intravascular ultrasound and optical coherence tomography images, where image artefacts such as acoustic shadow and calcification are commonplace and thus user guidance is desirable. We carried out both qualitative and quantitative analysis of the results for the medical data; comparing the proposed method against a number of interactive segmentation techniques.

“…The graph cuts techniques have been popularly applied for segmentation in numerous approaches, including image segmentation and volume segmentation . Essa et al applied graph cuts for the segmentation of intravascular ultrasound images, where the media‐adventitia border is automatically detected without user intervention, based on the minimization of a cost function derived from features like edge/boundary, shape prior, and texture. The authors in incorporated the edge‐based and region‐based constraints with graph cut and superpixel for interactive segmentation of the media‐adventitia border in intravascular ultrasound images and lumen border in optical coherence tomography images.…”

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.