Global Intensity Fitting Research Articles

AMSLS: Adaptive multi-scale level set method based on local entropy for image segmentation

Intensity inhomogeneity often appears in medical images and causes great difficulties in image segmentation. Most active contour models perform poorly when applied to intensity inhomogeneous images because their energy functions use local intensity information in a fixed-size domain, causing the contour to evolve in the wrong direction. To overcome the difficulties caused by intensity inhomogeneity, we propose an adaptive multi-scale Gaussian kernel function based on image local entropy, which can determine the appropriate scale for each local region. Choosing the small scale and large scale for inhomogeneous and homogeneous areas respectively make the contour move toward the target boundary accurately. We also propose three adaptive multi-scale (AMS) models, AMS-region scalable fitting (AMS-RSF) model, AMS-local image fitting (AMS-LIF) model, AMS-local and global intensity fitting (AMS-LGIF) model, to segment medical images with intensity inhomogeneity and noise, including left atrial MR images and breast ultrasound images. The experimental results show that the adaptive multi-scale Gaussian kernel function enables the active contour model to effectively segment intensity inhomogeneous images and has a certain robustness to the initial contour and noise, which achieves good performance on MR left atrial images and ultrasound images of breast cancer. The AMS-LGIF model obtained the highest DICE coefficient of 0.9532, which was better than the 0.9429 obtained by the second-ranked LGIF model to segment left atrial MR images. For segmenting breast ultrasound images, the DICE coefficient is increased by 16% than that of the U-Net++ model.

Comparative Analysis of Active Contour Models on Skin Cancer Images

Image segmentation is a focal endeavor in image examination accountable for partitioning an image into various sub-territories subject to a pined for the segment. A working structure is an essentialness constraining spline that recognizes demonstrated incorporates inside an image where it propels the shapes reliant on the check of imperativeness and fit it to the furthest reaches of inquiry within the image (thus, it can be used to customized objects division). Dynamic structures have been commonly used as engaging image division procedures since then for the most part, make sub-regions with constant points of confinement, while the piece based edge distinguishing proof methods, for example, Sobel edge discoverers, normally make convulsive breaking points. The utilization of level set hypothesis has given greater adaptability and comfort in the execution of dynamic shapes. Be that as it may, customary edge-based dynamic form models have been pertinent to just moderately straightforward images whose sub-districts are uniform without inward edges. Three ACMs namely Chan Vese (CV), Localized Active Contour (LAC) and Distance regularized level set (DRLS), Local binary fitting(LBF) and Local and global intensity fitting (LGIF) models analyzed and compared performance on skin cancer images. DSC values computed, compared and analyzed based on False Positive, True Positive and False Negative values.

An efficient level set method based on global statistical information for image segmentation

In this paper, an improved region-based active contour model in a novel variational level set formulation employing global information constraints is proposed for image segmentation. Using the image global characteristics, the proposed model can accurately segment objects with weak or blurred boundaries in the presence of noise or intensity inhomogeneity and achieve robustness against different kinds of images. We present a global intensity fitting energy functional based on the differences between the original image intensity and the global intensity means. This energy is then integrated into a geodesic variational level set formulation, from which a curve evolution equation is derived for energy minimization. The level set function is regularized by the Gaussian filter which keeps it smooth and eliminates the reinitialization. Experimental results on different images demonstrate the performance of our method in terms of accuracy and robustness. Compared with the well-known active contour models, our method is less sensitive to the initial contour and more computationally efficient.

A novel region-based active contour model based on kernel function for image segmentation

It is a difficult task to accurately segment images with intensity inhomogeneity, because most of existing algorithms are based upon the assumption of the homogeneity of image intensity. In this paper, we propose a novel region-based active contour model, referred to as the K-GLIF, which utilizes both global and local image intensity fittings with kernel functions. The model consists of an intensity fitting term and a new regularization term. The intensity fitting term of the level set function is the gradient descent flow that minimizes the global binary fitting energy functional. The local intensity fitting value based on the generalized Gaussian kernel function is then incorporated into the global intensity fitting value to form the weighted intensity fitting value on the two sides of the contour. Owing to the kernel function, the intensity information in local regions is extracted to guide the motion of the contour, which enables the model to effectively segment images with intensity inhomogeneity and smooth noise. A new regularization term is used to control the smoothness of the level set function and avoid complicated re-initialization. Experimental results and comparisons with other models of inhomogeneous images, synthetic images, medical images, multi-object images, natural and infrared images show that the proposed K-GLIF model improves the quality of image segmentation in terms of accuracy and robustness of initial contours.

A level set method for image segmentation based on Bregman divergence and multi-scale local binary fitting

Image segmentation is an important processing in many applications such as image retrieval and computer vision. The level set method based on local information is one of the most successful models for image segmentation. However, in practice, these models are at risk for existence of local minima in the active contour energy and the considerable computing-consuming. In this paper, a novel region-based level set method based on Bregman divergence and multi-scale local binary fitting(MLBF), called Bregman-MLBF, is proposed. Bregman-MLBF utilizes both global and local information to formulate a new energy function. The global information by Bregman divergence which can be approximated by the data-dependent weighted L2 − norm, not only accelerates the contour evolution, especially, when the contour is far away from object boundaries but also boosts the robustness to the initial placement. The local information is used to improve the capability of coping with intensity inhomogeneity and to attract the contour to stop at the object boundaries. The experiments conducted on synthetic images, real images and benchmark image datasets have demonstrated that Bregman-MLBF outperforms the piece-wise constant (PC) model in handling intensity inhomogeneity and is more effective than the local binary fitting model and more robust than the local and global intensity fitting model.

An Adaptive Stopping Active Contour Model for Image Segmentation

Active contour models (ACMs) are widely used in image segmentation applications. However, the selection of maximum iterations which controls the convergence of the ACMs is still a challenging problem. In this paper, an adaptive method for choosing the optimal number of iterations based on the local and global intensity fitting energy is proposed, which increases the automaticity of the active contour model. Moreover, the adoption of the reaction diffusion (RD) method instead of the distance regularization term can improve the accuracy and speed of segmentation effectively. Experimental results on synthetic and real images show that the proposed model outperforms other representative models in terms of accuracy and efficiency.

Morphological active contour driven by local and global intensity fitting for spinal cord segmentation from MR images

BackgroundSpinal cord (SC) segmentation from magnetic resonance (MR) images can be used to study neurological disorders and facilitates group analysis. Variation of intensity inhomogeneity and small cross section of SC are difficulties that restrict automizing SC segmentation. New methodsIn this paper we present a method for accurate SC segmentation from MR images. The proposed morphological local global intensity fitting model (MLGIF) is based on region based morphological active contour model that utilizes local and global information. The local information is obtained using local morphology fitting and has been embedded into region based active contour to deal with images intensity inhomogeneity and variable contrast levels between SC and the cerebrospinal fluid. The contour evolution has been performed using successive application of a set of morphological operators. ResultsThe proposed method has been validated on 28 T1-weighted and 29 T2-weighted MR images and simulated MR images with different noise levels. Assessment of the results shows the accuracy of the proposed method for SC segmentation. Comparison to existing method(s)The proposed MLGIF method was comparable with existing SC segmentation methods. Between segmented images and corresponding ground truth images, the mean DICE similarity coefficient, mean conformity coefficient and mean Hausdorff distance were 0.90 (092), 0.8 (0.83) and 0.85 mm (0.70 mm), respectively, for T1(T2)-weighted images. ConclusionThe MLGIF model was proposed to achieve a robust method to deal with intensity inhomogeneity and lack of contrast between SC and surrounding tissues. Moreover, accuracy and less sensitivity to initial curve were seen.

Automated Segmentation of Left Ventricle Using Local and Global Intensity Based Active Contour and Dynamic Programming

The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic resonance (MR) images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting (LGDF) energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract (LVOT) and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions (MICCAI) 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.978 1 and 0.956 7, for LV mass these values are 0.924 9 and 0.855 4. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.

Active contours using global models for medical image segmentation

Accurate segmentation with denoising is the subject of research in the field of medical imaging and computer vision. This paper presents an enhanced energy based active contour model with a level set detailing. Local energy fitting term impacts neighbourhood drive to pull the shape and restrict it to protest limits. Thus, the global intensity fitting term drives the movement of contour at distant from the object boundaries. The global energy term depends on worldwide division calculation, which can better catch energy data of picture than Chan-Vese (CV) model. Both neighbourhood and worldwide terms are commonly absorbed to build a vitality work in light of a level set plan to portion images with force inhomogeneity, experiments demonstrate that the proposed model has the upside of commotion resistance and is better than conventional image segmentation. Results demonstrate that the proposed method performs better both subjectively and quantitatively contrasted with other best in class methods.

Active contours using global models for medical image segmentation

Accurate segmentation with denoising is the subject of research in the field of medical imaging and computer vision. This paper presents an enhanced energy based active contour model with a level set detailing. Local energy fitting term impacts neighbourhood drive to pull the shape and restrict it to protest limits. Thus, the global intensity fitting term drives the movement of contour at distant from the object boundaries. The global energy term depends on worldwide division calculation, which can better catch energy data of picture than Chan-Vese (CV) model. Both neighbourhood and worldwide terms are commonly absorbed to build a vitality work in light of a level set plan to portion images with force inhomogeneity, experiments demonstrate that the proposed model has the upside of commotion resistance and is better than conventional image segmentation. Results demonstrate that the proposed method performs better both subjectively and quantitatively contrasted with other best in class methods.

Active contours driven by novel LGIF energies for image segmentation

An active contour model driven by novel local and global intensity fitting (LGIF) energies for image segmentation is presented. First, the GIF energy is defined by the squared-chord distance, which is more robust to noise. Second, the LIF energy is defined by the Lorentzian distance to calculate the local intensity information, which improves the generality of the presented model. Experiments are carried out on synthetic and real images and the results illustrate that the presented model can obtain higher segmentation accuracy and better segmentation efficiency; moreover, it is not sensitive to initial contour.

Adaptive active contour model driven by global and local intensity fitting energy for image segmentation

An adaptive active contour model combining global and local information is proposed for image segmentation in this paper. The energy functional consists of global term and local term, the global term is derived from the data fidelity item of CV model, and the local term introduces image local entropy which reflects the grey characteristics. Additionally, an adaptive method which takes the local information of the image into consideration is presented to set the parameters of global term and local term. The global term is able to segment homogeneous image rapidly and robust to segment noisy image; the local term can efficiently segment the intensity inhomogeneous image. By adaptively incorporating the global term with local term, the proposed model retains the advantages of both the global term and local term and widens the range of reasonable parameters. Experimental results on homogeneous images, intensity inhomogeneous images, noisy images and real images have demonstrated the efficiency and robustness of the proposed model.

Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation.

This paper introduces an improved region based active contour method with a level set formulation. The proposed energy functional integrates both local and global intensity fitting terms in an additive formulation. Local intensity fitting term influences local force to pull the contour and confine it to object boundaries. In turn, the global intensity fitting term drives the movement of contour at a distance from the object boundaries. The global intensity term is based on the global division algorithm, which can better capture intensity information of an image than Chan-Vese (CV) model. Both local and global terms are mutually assimilated to construct an energy function based on a level set formulation to segment images with intensity inhomogeneity. Experimental results show that the proposed method performs better both qualitatively and quantitatively compared to other state-of-the-art-methods.

An improved active contours model for image segmentation by level set method

The variational level set approaches are widely used in image segmentation. To extract the objects from the inhomogeneity image, an improved local and global binary fitting active contours model is presented in this paper. The energy functional has been defined by a local intensity fitting term and a global intensity fitting term. The generalized Gaussian distribution has been used as the kernel function of the local binary fitting information. The experiment results of the homogeneity images, artificial and real inhomogeneous images have shown the advantages of LGBF model in terms of accuracy and robustness.

Active contours driven by local and global intensity fitting energies based on local entropy

This paper proposes an improved region-based active contour model. A novel region-scalable fitting energy term which based on the local entropy deriving from a grey level distribution of image is defined. Simultaneously, an intensity fitting term that drives the motion of the active contour globally is added to the energy. The combination of these two terms is then incorporated into a variational level set formulation with two extra regularization terms that are necessary for accurate computation in the corresponding level set method. The resulting evolution of the level set function is the gradient flow that minimizes the overall energy functional. The proposed method has been tested on synthetic and real images, and the experiment results show that our method is efficient and robust for segmenting images with noisy and intensity inhomogeneity.

Convex active contour model for target detection in synthetic aperture radar images

The active contour model (ACM) is one of the most successful methods for target detection in optical and medical images, but multiplicative speckle noise greatly interferes with its use in synthetic aperture radar (SAR) images. To overcome this difficulty, a convex ACM is proposed. First, a ratio distance is defined in terms of the probability density functions on both sides of the contour. This ratio is then introduced into the Chan and Vese (CV) model. Second, by combining the modified CV and region-scalable fitting models, the global intensity fitting and local intensity fitting forces both evolve the contour. Third, the energy of the combination model is then incorporated into Chan’s global minimization active contour framework so that the global minimum can be reached. Furthermore, the gradient term in the final energy function allows the proposed model to locate boundaries more quickly and accurately. Numerically, a dual formulation is utilized to solve the problem of active contour propagation toward target boundaries. Target detection experiments in real and simulated SAR images show that the proposed model outperforms classical region-based and hybrid ACMs in terms of efficiency and accuracy.

A novel active contour model driven by local and global intensity fitting energies

In this paper, we propose a novel hybrid active contour model for image segmentation. In our model, we define a new region-scalable fitting (RSF) energy functional which combines the local and the global image information. The RSF energy functional can not only attract the contour toward object boundaries, but also improve the robustness to initialization of the contours. In order to segment the image fast and accurately, the length term and regularization term is incorporated into the variational level set formulation. Finally, by adopting gradient descent method, the minimization of the energy equation can be given. Due to the new kernel function we defined, our model can cope with intensity inhomogeneity images and less sensitive to the initialization of the contour when compared with the other models. Experimental results demonstrated that the proposed model can also segment both the real and medical images accurately.

An adaptive weighting parameter estimation between local and global intensity fitting energy for image segmentation

Local and global intensity fitting energy are widely used for image segmentation. In order to improve the segmentation quality in the presence of intensity inhomogeneity, in this paper, we propose a new adaptive rule for obtaining weighting parameter estimation between the local and global intensity fitting energy. Following the minimization of the energy functional, the value of the weighting parameter is dynamically updated with the contour evolution, which is effective and accurate for extracting the object.

Automatic segmentation of left ventricle endocardium from cardiac MR images using active contours driven by local and global intensity fitting energy

In this paper, we present a fully automated method for segmenting left ventricle endocardium from multi slice cine short axis cardiac MR images. Our method does not require manually drawn initial contour and is able to segment images in the presence of noise and intensity inhomogeneity. The segmentation process flow uses temporal variance of image intensity to localise the heart region. Slices are segmented sequentially using a local and global statistics-based active contour model. To control the influence of the global energy, an adaptive weight function that varies dynamically with image region is applied. The method was tested on a database of 30 cases obtained from the Sunnybrook Health Sciences Centre, and the results were compared with manual delineated ground truth. The algorithm’s performance is evaluated using two metrics, average perpendicular distance (APD) and dice similarity coefficient (DSC). Resulting contours show a mean DSC of 0.88 and an overall APD around 2 mm. Linear regression analysis of ejection fraction (EF) yielded a slope 1.015 and R2 = 0.926. The proposed segmentation approach shows a better performance and provides a practical method for use in clinical practice.

Level set method coupled with Energy Image features for brain MR image segmentation

Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels' values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods: one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.