Optimal multi-object segmentation with novel gradient vector flow based shape priors

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.

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“…where w(i) = D i (1) − D i (0), and t(i, j) = T ij (1,0). As shown in another research, 32 Equations 4 and 7 are equivalent.…”

Section: Minimum S-e Graph Optimizationmentioning

confidence: 75%

“…

frakturE false(S false) = \sum_{i \in S} w false(i false) + \sum_{\frac{0}{false(i, j false) \in E}} t false(i, j false),

where w ( i ) = D i (1) − D i (0), and t ( i , j ) = T ij (1,0). As shown in another research, Equations and are equivalent.…”

Section: Proposed Methodsmentioning

confidence: 99%

Coupled s‐excess HMM for vessel border tracking and segmentation

Essa

Jones

Xie

2019

Numer Methods Biomed Eng

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“…Several authors have explored the topic of preventing overlap in graph-based multi-object segmentation [1,7,12,20,25]. The key challenge is that graph cuts can only be used to minimize so-called submodular energy functions.…”

Section: Approaches For Preventing Overlapmentioning

confidence: 99%

Multi-object Graph-based Segmentation with Non-overlapping Surfaces

Jensen

Dahl

2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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For 3D images, segmentation via fitting surface meshes to object boundaries provides an efficient way to handle large images and enforce geometric prior knowledge. Furthermore, fitting such meshes with graph cuts has proven to be a versatile and robust framework. However, when segmenting multiple distinct objects in one image, current methods do not allow the natural constraint that objects should not overlap. In this paper, we present an extension to graph cut based methods which can provide a globally optimal segmentation of thousands of objects while guaranteeing no overlap. Our method works by separating objects with planes whose positions are determined as part of the graph cut. To demonstrate the general applicability of our method, we apply it to several 3D microscopy data sets from both biology and materials science. Our results show both quantitative and qualitative improvements.

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“…This technique minimizes an energy function to drive the initial contour to reach the boundary of the target region, to extract the RoIs. According to the different contour construction modes, it can be divided into parametric active contour model [8][9][10][11][12][13] and geometric active contour model .…”

Section: Introductionmentioning

confidence: 99%

A hybrid level set model for image segmentation

et al. 2021

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Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and local image information. The local information of the model is obtained by bilateral filters, which can also enhance the edge information while smoothing the image. The local fitting centers are calculated before the contour evolution, which can alleviate the iterative process and achieve fast image segmentation. The global information of the model is obtained by simplifying the C-V model, which can assist contour evolution, thereby increasing accuracy. Experimental results show that our algorithm is insensitive to the initial contour position, and has higher precision and speed.

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Optimal multi-object segmentation with novel gradient vector flow based shape priors

Cited by 12 publications

References 57 publications

Coupled s‐excess HMM for vessel border tracking and segmentation

Coupled s‐excess HMM for vessel border tracking and segmentation

Multi-object Graph-based Segmentation with Non-overlapping Surfaces

A hybrid level set model for image segmentation

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