A hybrid level set model for image segmentation

Abstract

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.

Automatic liver tumor segmentation and identification using fully connected convolutional neural network from CT images

“…The idea behind this method is to present the curves or surfaces as the zero‐level set. It means closed curves enclosed in the two‐dimensional surface are regarded as a continuous surface of three‐dimensional space 25 $$$$ \phi\ \left(\mathrm{x},\mathrm{y},\mathrm{t}\right)=0, $$$$ $$$$ {\phi}_t+F\mid \nabla \phi \mid =0, $$$$ where is the speed of the function depending on evolving the surface and image characterization?…”

“…It means closed curves enclosed in the two-dimensional surface are regarded as a continuous surface of three-dimensional space. 25 𝜙 (x, y, t) = 0, (…”

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“…When the energy functional is a convex function, when the gradient descent method is used to solve the minimum value of the function, the unique value obtained is different from the initial condition. When the energy functional is non-convex and the gradient descent method is used to solve the minimum value of the function, the solution obtained is not unique, and different initial values may result in different local minimums [ 23 , 24 , 25 ]. In the existing segmentation models, most segmentation methods use direct regular models to avoid level set re-initialization, such as distance regular level set model (DRLSE) evolution [ 26 ].…”

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“…When realizing pattern recognition problems within the realm of computer vision, various algorithms can be employed. These encompass rule-based algorithms, which consists of contour analysis, snakes, region growing, Markov chains, level set methods, and graphs, as referenced in [9][10][11][12][13][14]. Additionally, there are Artificial Intelligence (AI) approaches, which involve Machine Learning (ML) and Deep Learning (DL) algorithms, as depicted in [15][16][17].…”

“…However, they are susceptible to specific implementation conditions, such as confined environments and standardized lighting conditions. As indicated in [9][10][11][12][13][14][15][16][17][18][19],…”