Effective Image Segmentation using Composite Energy Metric in Levelset Based Curve Evolution

Accurate segmentation of anatomical organs in medical images is a complex task due to wide inter-patient variability and several acquisition dependent artefacts. Moreover, image noise, low contrast and intensity inhomogeneity in medical data further amplifies the challenge. In this work, we propose an effective yet simple algorithm based on composite energy metric for precise detection of object boundaries. A number of methods have been proposed in literature for image segmentation; however, these methods employ individual characteristics of image including gradient, regional intensity or texture map. Segmentation based on individual featres often fail for complex images, especially for medical imagery. Accordingly, we propose that the segmentation quality can be improved by integrating local and global image features in the curve evolution. This work employs the classic snake model aka active contour model; however, the curve evolution force has been updated. In contast to the conventional image-based regional intensity statistics, the proposed snake model evolves using composite image energy. Hence, the proposed method offers a greater resistance to the local optima problem as well as initialization perturbations. Experimental results for both synthetic and 2D (Two Dimensional) real clinal images are presented in this work to validate the performance of the proposed method. The performance of the proposed model is evaluated with respect to expert-based manual ground truth. Accordingly, the proposed model achieves higher accuracy in comparison to the state-of-the-art region based segmentation methods of Lankton and Yin as reported in results section.

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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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Narrow Band Region-Based Active Contours Model for Noisy Color Image Segmentation

The Scientific World JOURNAL ◽

10.1155/2014/307416 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Xiaomin Xie ◽

Aijun Zhang ◽

Changming Wang ◽

Xiangfei Meng

Keyword(s):

Image Segmentation ◽

Active Contours ◽

Narrow Band ◽

Color Image ◽

Active Contour Model ◽

Color Image Segmentation ◽

Superior Performance ◽

Combination Method ◽

Proposed Model ◽

Level Set Framework

A narrow band active contour model for color image segmentation is proposed, which applies local statistics to improve the robustness against noise. The crux of our approach is to use intensity mean of local region to define the force function within a level set framework, within which a narrow band is implemented to further improve the computational efficiency. In addition, the image is segmented from channel-to-channel, which shows superior performance when the intensities of the object and background are similar. Furthermore, a multichannel segmentation combination method is used to integrate the information of multiple level sets. The proposed model has been applied to both synthetic and real images with expected results, and the comparison with the state-of-the-art approaches demonstrates the accuracy and superiority of our approach.

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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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An Effective X-Ray Image Segmentation Method for the Pharmaceutical Industry via Surface Fitting

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.839 ◽

2013 ◽

Vol 333-335 ◽

pp. 839-844

Author(s):

Kai Hong Shi ◽

Zong Qing Lu ◽

Qing Min Liao

Keyword(s):

Image Segmentation ◽

Pharmaceutical Industry ◽

Active Contour Model ◽

Three Dimensional ◽

Surface Fitting ◽

Segmentation Method ◽

Image Subtraction ◽

Low Contrast ◽

X Ray ◽

Fixed Condition

Image segmentation techniques currently used for X-ray inspection in pharmaceutical industry suffer from some limitations. The object in an image is close to the background and its contours are weak or blurred because of the X-ray imaging characteristic. Based on our research of X-ray inspection, a simple and efficient image segmentation method is proposed in this paper. It is implemented by treating the image and desired contours as three dimensional surface and holes respectively in order to simplify the model of segmentation, and making use of surface fitting and image subtraction to extract the target region efficiently. The novelty of this approach is that we need less selection of parameters to extract contours with low contrast by surface fitting. Experiments on real X-ray images demonstrate the advantages of the proposed method over active contour model (ACM) and Chan_Vese model (CV model) in terms of both accuracy and efficiency on fixed condition.

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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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Interactive Medical Image Segmentation Using Snake and Multiscale Curve Editing

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/325903 ◽

2013 ◽

Vol 2013 ◽

pp. 1-13 ◽

Cited By ~ 15

Author(s):

Wu Zhou ◽

Yaoqin Xie

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Medical Image ◽

Surgical Planning ◽

Active Contour Model ◽

Clinical Applications ◽

Medical Image Segmentation ◽

Interactive Segmentation ◽

Snake Model ◽

Segmentation Methods

Image segmentation is typically applied to locate objects and boundaries, and it is an essential process that supports medical diagnosis, surgical planning, and treatments in medical applications. Generally, this process is done by clinicians manually, which may be accurate but tedious and very time consuming. To facilitate the process, numerous interactive segmentation methods have been proposed that allow the user to intervene in the process of segmentation by incorporating prior knowledge, validating results and correcting errors. The accurate segmentation results can potentially be obtained by such user-interactive process. In this work, we propose a novel framework of interactive medical image segmentation for clinical applications, which combines digital curves and the active contour model to obtain promising results. It allows clinicians to quickly revise or improve contours by simple mouse actions. Meanwhile, the snake model becomes feasible and practical in clinical applications. Experimental results demonstrate the effectiveness of the proposed method for medical images in clinical applications.

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Adaptively Active Contours Based on Variable ExponentLp(|∇I|)Norm for Image Segmentation

Mathematical Problems in Engineering ◽

10.1155/2012/490879 ◽

2012 ◽

Vol 2012 ◽

pp. 1-20 ◽

Cited By ~ 4

Author(s):

Wenying Wen ◽

Chuanjiang He ◽

Meng Li ◽

Yi Zhan

Keyword(s):

Image Segmentation ◽

Internal Energy ◽

Variable Exponent ◽

Active Contours ◽

Experimental Results ◽

External Energy ◽

Low Contrast ◽

Image Gradient ◽

Gradient Information ◽

Proposed Model

We propose anLp(|∇I|)-based adaptively active contours model for image segmentation which is derived from the well-known Chan-Vese (C-V) model. Unlike the C-V model, the proposed model uses theLp(|∇I|) (p(|∇I|)>2)norm instead of theL2norm to define the external energy and incorporates an extra internal energy into the overall energy. Due to the variable exponentp(|∇I|) which could fit the image gradient information adaptively, the proposedLp(|∇I|)-based model has the hope of segmenting those images with low contrast and blurred boundaries. Experimental results show that the proposed model withp(|∇I|)>2really can effectively and quickly segment those images with low contrast and blurred boundaries.

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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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