scholarly journals Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Asif Aziz Memon ◽  
Shafiullah Soomro ◽  
Muhammad Tanseef Shahid ◽  
Asad Munir ◽  
Asim Niaz ◽  
...  
Keyword(s):  
Weight Function ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Energy Functional ◽  
Intensity Level ◽  
Contour Model ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Intensity Functions

Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images. Based on a quantitative analysis over the mini-MIAS and PH2 databases, the superiority of the proposed model in terms of segmentation accuracy, as compared with the ground truths, was confirmed. Furthermore, when using the proposed model, the processing time for image segmentation is lower than those when using other methods.

scholarly journals Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Tingting Liu ◽  
Haiyong Xu ◽  
Wei Jin ◽  
Zhen Liu ◽  
Yiming Zhao ◽  
...  
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Energy Functional ◽  
Medical Image Segmentation ◽  
Regularization Term ◽  
Contour Model ◽  
Proposed Model ◽  
Hybrid Region

A novel hybrid region-based active contour model is presented to segment medical images with intensity inhomogeneity. The energy functional for the proposed model consists of three weighted terms: global term, local term, and regularization term. The total energy is incorporated into a level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Experiments on some synthetic and real images demonstrate that our model is more efficient compared with the localizing region-based active contours (LRBAC) method, proposed by Lankton, and more robust compared with the Chan-Vese (C-V) active contour model.

A Local Statistical Information Active Contour Model for Image Segmentation

2014 ◽  
Vol 6 (2) ◽  
pp. 33-49 ◽  
Author(s):  
Shigang Liu ◽  
Yali Peng ◽  
Guoyong Qiu ◽  
Xuanwen Hao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Statistical Information ◽  
Intensity Inhomogeneity ◽  
Image Domain ◽  
Contour Model ◽  
Proposed Model ◽  
Spatially Varying ◽  
Level Set Formulation

This paper presents a local statistical information (LSI) active contour model. Assuming that the distribution of intensity belonging to each region is a Gaussian distribution with spatially varying statistical information, and defining an energy function, the authors integrate the entire image domain. Then, this energy is incorporated into a variational level set formulation. Finally, by minimizing the energy functional, a curve evolution equation can be obtained. Because the image local information is considered, the proposed model can effectively deal with the image with intensity inhomogeneity. Experimental results on synthetic and real images demonstrate that the proposed model can effectively segment the image with intensity inhomogeneity.

IMAGE COSEGMENTATION BASED ON LOCAL AND GLOBAL LEVEL SET METHODS

2012 ◽  
Vol 12 (03) ◽  
pp. 1250019 ◽  
Author(s):  
LIHE ZHANG ◽  
ZHENZHEN LIU
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Level Set Methods ◽  
Energy Functional ◽  
Image Statistics ◽  
Contour Model ◽  
Contour Models ◽  
Homogeneous Regions ◽  
The Given ◽  
Edge Indicator Function

In this paper, we propose a novel cosegmentation algorithm based on active contour model which utilizes local and global image statistics. Many localized region-based active contour models have been proposed to solve a challenging problem of the property (such as intensity, color, texture, etc.) inhomogeneities that often occurs in real images, but these models usually cannot reasonably evolve the curve in this situation that some center points along the curve are in homogeneous regions and their local regions are far away from the object. In order to overcome the difficulties we selectively enlarge the driven force of some points and introduce the edge indicator function to avoid the curve over-shrinking or over-expanding on the salient boundaries. In addition, we introduce global image statistics to better the curve evolution and try to avoid the given energy functional converging to a local minimum. Practical experiments show that our algorithm can obtain better segmentation results.

scholarly journals Multiple Active Contours Driven by Particle Swarm Optimization for Cardiac Medical Image Segmentation

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
I. Cruz-Aceves ◽  
J. G. Aviña-Cervantes ◽  
J. M. López-Hernández ◽  
S. E. González-Reyna
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contours ◽  
Active Contour Model ◽  
Particle Swarm ◽  
Magnetic Resonance Images ◽  
Swarm Optimization ◽  
Contour Model

This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability.

A SEGMENTATION FRAMEWORK FOR PHASE CONTRAST AND FLUORESCENCE MICROSCOPY IMAGES

2014 ◽  
Vol 28 (07) ◽  
pp. 1460013 ◽  
Author(s):  
MITCHEL ALIOSCHA-PEREZ ◽  
RONNIE WILLAERT ◽  
HICHEM SAHLI
Keyword(s):  
Active Contour ◽  
Phase Contrast ◽  
Active Contour Model ◽  
Region Of Interest ◽  
Energy Functional ◽  
Living Cells ◽  
Shape Fitting ◽  
Contour Model ◽  
Microscopy Images

The noninvasive imaging of unstained living cells is a widely used technique in biotechnology for determining biological and biochemical role of proteins, since it allows studying living specimens without altering them. Usually, fluorescence and contrast (or transmission) images are both used complementarily, as their combination allows possible better outcomes. However, segmentation of contrast images is particularly difficult due to the presence of defocused scans, lighting/shade-off artifacts and cells overlapping. In this work, we investigate the optical properties intervening during the image formation process, and propose different segmentation strategies that can benefit from these properties. The proposed scheme (i) combines the estimated phase and the fluorescence information in order to obtain initial markers for a latter segmentation stage; and (ii) use the shear oriented polar snakes, an active contour model that implicitly involves phase information on its energy functional. The obtained contour can be used as region of interest estimation, as data for a latter shape-fitting process, or as smart markers for a more detailed segmentation process (i.e. watershed). Experimental results provide a comparison of the different segmentation schemes, and confirm the suitability of the proposed strategy and model for cell images segmentation.

EFFICIENT ACTIVE CONTOUR MODEL FOR MULTIPHASE SEGMENTATION WITH APPLICATION TO BRAIN MR IMAGES

2013 ◽  
Vol 27 (01) ◽  
pp. 1355001 ◽  
Author(s):  
YUNYUN YANG ◽  
YI ZHAO ◽  
BOYING WU
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Energy Model ◽  
Mr Images ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Contour Model ◽  
New Energy ◽  
Brain Mr Images

In this paper, we propose an efficient active contour model for multiphase image segmentation in a variational level set formulation. By incorporating the globally convex segmentation idea and the split Bregman method into the multiphase formulation of the local and global intensity fitting energy model, our new model improved the original local and global intensity fitting energy model in the following aspects. First, we propose a new energy functional using the globally convex segmentation method to guarantee fast convergence. Second, we incorporate information from the edge into the energy functional by using a non-negative edge detector function to detect boundaries more easily. Third, instead of a constant value to control the influence of the local and global intensity fitting terms, we use a weight function varying with the locations of the image to balance the weights between the local and the global fitting terms dynamically. Lastly, the special structure of our energy functional enables us to apply the split Bregman method to minimize the energy much more efficiently. We have applied our model to synthetic images and real brain MR images with promising results. Experimental results demonstrate the efficiency and superiority of our model.

A New Image Segmentation Model

2014 ◽  
Vol 519-520 ◽  
pp. 541-547
Author(s):  
Chao Liu ◽  
Jing Liu ◽  
Lu Lu Zhang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Weighted Average ◽  
Mean Value ◽  
Local Information ◽  
Experimental Result ◽  
New Model ◽  
Contour Model ◽  
Segmentation Accuracy

To build a new image segmentation model based on level set theory : Add edge detection operator to edgeless active contour model to detect local information; introduce adaptive coefficient of area item to let the model autonomously adjust and evolve curve position according to image information; adopt weighted average gray value to replace traditional absolute mean value to reduce error and improve segmentation result. Experimental result comparison shows that the new model can detect global information and local information at the same time, adaptively adjust curve evolution direction, and has a fast segmentation speed. Compared to edgeless active contour model, the new model is a more effective image segmentation method as it has greater advantages in image segmentation accuracy and computational complexity.

scholarly journals 3D segmentation of thyroid ultrasound images using active contours

2016 ◽  
Vol 2 (1) ◽  
pp. 467-470 ◽  
Author(s):  
Prabal Poudel ◽  
Christian Hansen ◽  
Julian Sprung ◽  
Michael Friebe
Keyword(s):  
Active Contour ◽  
3D Model ◽  
Active Contours ◽  
Active Contour Model ◽  
Thyroid Volume ◽  
Reconstruction Algorithm ◽  
Ultrasound Images ◽  
3D Segmentation ◽  
Segmentation Accuracy ◽  
2D Ultrasound

AbstractIn this paper, we propose a method to segment the thyroid from a set of 2D ultrasound images. We extended an active contour model in 2D to generate a 3D segmented thyroid volume. First, a preprocessing step is carried out to suppress the noise present in US data. Second, an active contour is used to segment the thyroid in each of the 2D images. Finally, all the segmented thyroid images are passed to a 3D reconstruction algorithm to obtain a 3D model of the thyroid. We obtained an average segmentation accuracy of 86.7% in six datasets with a total of 703 images.

scholarly journals Implicit Active Contour Model with Local and Global Intensity Fitting Energies

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaozeng Xu ◽  
Chuanjiang He
Keyword(s):  
Steady State ◽  
Initial Function ◽  
Active Contour ◽  
Active Contour Model ◽  
Level Line ◽  
Zero Level ◽  
Contour Model ◽  
Proposed Model ◽  
Local Intensity ◽  
Global Image Information

We propose a new active contour model which integrates a local intensity fitting (LIF) energy with an auxiliary global intensity fitting (GIF) energy. The LIF energy is responsible for attracting the contour toward object boundaries and is dominant near object boundaries, while the GIF energy incorporates global image information to improve the robustness to initialization of the contours. The proposed model not only can provide desirable segmentation results in the presence of intensity inhomogeneity but also allows for more flexible initialization of the contour compared to the RSF and LIF models, and we give a theoretical proof to compute a unique steady state regardless of the initialization; that is, the convergence of the zero-level line is irrespective of the initial function. This means that we can obtain the same zero-level line in the steady state, if we choose the initial function as a bounded function. In particular, our proposed model has the capability of detecting multiple objects or objects with interior holes or blurred edges.

scholarly journals Fractional Differentiation-Based Active Contour Model Driven by Local Intensity Fitting Energy

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ming Gu ◽  
Renfang Wang
Keyword(s):  
Fractional Order ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Input Image ◽  
Intensity Inhomogeneity ◽  
Initial Contour ◽  
Level Set Function ◽  
Contour Model ◽  
Proposed Model

A novel active contour model is proposed for segmentation images with inhomogeneity. Firstly, fractional order filter is defined by eight convolution masks corresponding to the image orientation in the eight compass directions. Then, the fractional order differentiation image is obtained and applied to the level set method. Secondly, we defined a new energy functional based on local image information and fractional order differentiation image; the proposed model not only can describe the input image more accurately but also can deal with intensity inhomogeneity. Local fitting term can enhance the ability of the model to deal with intensity inhomogeneity. The defined penalty term is used to reduce the occurrence of false boundaries. Finally, in order to eliminate the time-consuming step of reinitialization and ensure stable evolution of level set function, the Gaussian filtering method is used. Experiments on synthetic and real images show that the proposed model is efficient for images with intensity inhomogeneity and flexible to initial contour.

Sign in / Sign up

Export Citation Format

Share Document