An improved model for joint segmentation and registration based on linear curvature smoother

Image segmentation and registration are two of the most challenging tasks in medical imaging. They are closely related because both tasks are often required simultaneously. In this article, we present an improved variational model for a joint segmentation and registration based on active contour without edges and the linear curvature model. The proposed model allows large deformation to occur by solving in this way the difficulties other jointly performed segmentation and registration models have in case of encountering multiple objects into an image or their highly dependence on the initialisation or the need for a pre-registration step, which has an impact on the segmentation results. Through different numerical results, we show that the proposed model gives correct registration results when there are different features inside the object to be segmented or features that have clear boundaries but without fine details in which the old model would not be able to cope.

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Convex Image Segmentation Model Based on Local and Global Intensity Fitting Energy and Split Bregman Method

Journal of Applied Mathematics ◽

10.1155/2012/692589 ◽

2012 ◽

Vol 2012 ◽

pp. 1-16 ◽

Cited By ~ 12

Author(s):

Yunyun Yang ◽

Boying Wu

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Energy Functional ◽

Split Bregman Method ◽

Split Bregman ◽

Proposed Model ◽

Variational Level Set ◽

Convex Image Segmentation ◽

Level Set Formulation ◽

Global Fitting

We propose a convex image segmentation model in a variational level set formulation. Both the local information and the global information are taken into consideration to get better segmentation results. We first propose a globally convex energy functional to combine the local and global intensity fitting terms. The proposed energy functional is then modified by adding an edge detector to force the active contour to the boundary more easily. We then apply the split Bregman method to minimize the proposed energy functional efficiently. By using a weight function that varies with location of the image, the proposed model can balance the weights between the local and global fitting terms dynamically. We have applied the proposed model to synthetic and real images with desirable results. Comparison with other models also demonstrates the accuracy and superiority of the proposed model.

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A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/7595174 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18 ◽

Cited By ~ 1

Author(s):

Chaolu Feng ◽

Jinzhu Yang ◽

Chunhui Lou ◽

Wei Li ◽

Kun Yu ◽

...

Keyword(s):

Image Segmentation ◽

Level Set ◽

Bias Correction ◽

Active Contour Model ◽

Multiple Objects ◽

Level Set Function ◽

Proposed Model ◽

Segmentation Accuracy ◽

Term Energy ◽

Set Function

Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.

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A Hybrid Active Contour Model based on New Edge-Stop Functions for Image Segmentation

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2020010105 ◽

2020 ◽

Vol 11 (1) ◽

pp. 87-98 ◽

Cited By ~ 8

Author(s):

Xiaojun Yang ◽

Xiaoliang Jiang

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Active Contour Model ◽

Variational Model ◽

Local Means ◽

Area Of Interest ◽

Complex Shapes ◽

Sharp Edges ◽

Non Local ◽

Edge Based

Edge-based active contour methods are popular algorithms for image segmentation, with the purpose to extract the area of interest. However, they may face to boundary leakage and improper segmentation when handle images under weak edges or complex shapes. The extensive edge-stop functions adopt edge information, which cannot apply to guide the evolving curve approaching to target boundaries. To resolve this issue, a novel level set algorithm based on non-local means (NLM) filtering is constructed in this study. Firstly, the images are subjected to non-local means filtering to generate edge map. Secondly, a new edge-stop function constructed from this edge map as well as the fuzzy k-NN classification algorithm is incorporated into the variational model. Our experiments demonstrate that non-local means filtering is able to sharp edges both on medical and natural images. Thus, this analysis seems to be useful for clinical medical diagnosis.

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Composite fuzzy-wavelet-based active contour for medical image segmentation

Engineering Computations ◽

10.1108/ec-11-2019-0529 ◽

2020 ◽

Vol 37 (9) ◽

pp. 3525-3541

Author(s):

Hiren Mewada ◽

Amit V. Patel ◽

Jitendra Chaudhari ◽

Keyur Mahant ◽

Alpesh Vala

Keyword(s):

Image Segmentation ◽

Level Set ◽

Legendre Polynomial ◽

Active Contour ◽

Medical Image ◽

Medical Images ◽

Medical Image Segmentation ◽

Low Resolution ◽

Content Type ◽

Proposed Model

Purpose In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images. Design/methodology/approach The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach. Findings The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%. Originality/value The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.

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A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation

The Scientific World JOURNAL ◽

10.1155/2014/840305 ◽

2014 ◽

Vol 2014 ◽

pp. 1-19 ◽

Cited By ~ 2

Author(s):

Jiao Shi ◽

Jiaji Wu ◽

Anand Paul ◽

Licheng Jiao ◽

Maoguo Gong

Keyword(s):

Image Segmentation ◽

Real World ◽

Active Contour ◽

Active Contour Model ◽

Intensity Inhomogeneity ◽

Curve Evolution ◽

Evolution Theory ◽

Piecewise Constant ◽

Contour Model ◽

Proposed Model

Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images.

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Some numerical methods for solving geodesic active contour model on image segmentation process

Malaysian Journal of Fundamental and Applied Sciences ◽

10.11113/mjfas.v13n4-1.849 ◽

2017 ◽

Vol 13 (4-1) ◽

pp. 408-411

Author(s):

Maizatul Nadirah Mustaffa ◽

Norma Alias ◽

Faridah Mustapha

Keyword(s):

Image Segmentation ◽

Numerical Methods ◽

Active Contour ◽

Active Contour Model ◽

Central Difference ◽

Geodesic Active Contour ◽

Contour Model ◽

Proposed Model ◽

Difference Formula ◽

Edge Based

In this paper, we present an edge-based image segmentation technique using modified geodesic active contour model to detect the desired objects from an image. The stopping function of the proposed model has been modified from the usual geodesic active contour model. The modified geodesic active contour model is discretized using finite difference method based on the central difference formula. Then, some numerical methods such as RBGS and Jacobi methods are used for solving the linear system of equation. The accuracy and effectiveness of the proposed algorithm have been illustrated by applied to different images and some numerical methods.

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A geodesic-active-contour-based variational model for short-axis cardiac MR image segmentation

International Journal of Computer Mathematics ◽

10.1080/00207160.2012.695355 ◽

2013 ◽

Vol 90 (1) ◽

pp. 124-139 ◽

Cited By ~ 9

Author(s):

Wei Zhu ◽

Sung Ha Kang ◽

George Biros

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Variational Model ◽

Short Axis ◽

Mr Image ◽

Geodesic Active Contour ◽

Cardiac Mr ◽

Mr Image Segmentation

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IMAGE SEGMENTATION WITH GVF FLOW AND DIRECTIONAL INFORMATION

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691312500245 ◽

2012 ◽

Vol 10 (03) ◽

pp. 1250024

Author(s):

ZHONGHUA LUO ◽

JITAO WU

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Active Contour Model ◽

Intensity Inhomogeneity ◽

Initial Curve ◽

Directional Information ◽

Contour Model ◽

Model Combining ◽

Proposed Model ◽

Edge Location

Intensity inhomogeneity is a common phenomenon in real-world images and may cause many difficulties in image segmentation. To overcome these difficulties, we propose a new active contour model combining the GVF flow and the directional information about edge location. On one hand, we incorporate the GVF flow into the proposed model to segment the images with intensity inhomogeneity efficiently. On the other hand, we construct an alignment term with the directional information to achieve sub-pixel accuracy and relax the placement of the initial curve. Moreover, a regularization term is also included in our model to ensure accurate computation and avoid time-consuming re-initializations. Experimental results on several synthetic and real images show that the proposed model is effective.

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