Local Gaussian Distribution Fitting Energy Research Articles

Local Gaussian Distribution Fitting Boundary Image Segmentation Algorithm for Ultrasound Images in Avoiding Recurrent Laryngeal Nerve Injury during Thyroid Nodules Treatment

This work aimed to analyze the effect of the boundary segmentation algorithm in thyroid nodules image segmentation and the influence of its adoption in avoiding recurrent laryngeal nerve (RLN) injury during treatment of thyroid nodules. The nodule boundary was extracted aided by the local Gaussian distribution fitting energy (LGDF) segmentation algorithm, which was compared with the normalized cut (Ncut) algorithm and the Canny algorithm. Then, 51 patients treated with microwave ablation for thyroid nodules were taken as a test group, and 51 patients treated with surgical resection were taken as a control group. The incidence of RLN injury and the levels of free triiodothyronine (FT3), free thyroid hormone (FT4), and thyroid-stimulating hormone (TSH) were compared between the two groups before and after treatment. The results showed that the true positive fraction (TPF) of the LGDF segmentation algorithm was 69.45%, the TPF of the Ncut algorithm and the Canny algorithm were 58.65% and 52.37%, respectively. The TPF of LGDF algorithm was higher than that of the Ncut algorithm and Canny algorithm, with notable differences ( P < 0.05 ). In the control group, there were 10 cases of temporary and permanent damage to the RLN after operation, and the total incidence was 19.61%. In the test group, there were 3 cases of temporary and permanent damage to the RLN after operation, and the total incidence was 5.88%, which was lower than that of the control group ( P < 0.05 ). No evident differences were shown in the levels of FT3, FT4, and TSH between the two groups before treatment ( P > 0.05 ). However, after treatment, the TSH level of the test group (4.58 ± 0.79) was higher than that of the control group (3.19 ± 0.17), and the levels of FT3 and FT4 in the test group were lower than those in the control group, and the differences were remarkable ( P < 0.05 ). In short, the LGDF algorithm had more ideal segmentation effect. In addition, ultrasound-guided microwave ablation was effective in treating benign thyroid nodules, which could reduce damage to the RLN and maintain normal thyroid function.

Automatic initialization for active contour models based on particle swarm optimization and application to medical images

The active contour models (ACMs) are one of the most widely used techniques in image segmentation, localization and object tracking. Although there are several existing updated versions of ACMs, in which most of the models do not converge to the desired results in images due to complex background and depends on the initial placement of contour. Among the methods based on the level set, the Local Gaussian Distribution Fitting (LGDF) Energy and the Local Binary Fitting (LBF) Energy are successful algorithms, that suffers from image contours and initial position selection, which are the considerable demerits of the models. To overcome these adversities, we present an efficient and socially-inspired population based stochastic algorithm called particle swarm contour search (PSCS) which is the modified version of particle swarm optimization (PSO) algorithm, considered to be one of the most important optimization methods in swarm intelligence. Firstly, we apply smoothing filters on image to remove high intensities noise. Secondly, we utilized the PSCS algorithm to find the dominant points around the object’s boundaries. The PSCS selects some extra points in different parts of the image rather than the required object, such points are removed in our post PSCS step by utilizing different morphological operations. Furthermore, we calculate the center position and radius of the object for initial contour with the help of points generated using PSCS. The experimental outcome of the segmentations indicate that our proposed approach is automatic and fast for its initialization and successfully segment the desire object in medical images.

Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology

Image segmentation is a key technology in the field of computer image processing. Among them, segmentation methods based on active contour models have been developed rapidly in recent years due to their effective processing of complex images such as medical images. These methods have achieved significant results in medical, military, and industrial fields. Present research work mainly introduces the training of computer graphics and image processing technology and the method of active contour image segmentation. It focuses on the study of image segmentation methods and focuses on the segmentation methods based on active contour models. Firstly, it summarizes two types of segmentation methods based on edge and region and summarizes their advantages and disadvantages. Then, the segmentation method based on the active contour model is studied, and several typical active contour models are comprehensively compared. Finally, the local binary fitting model and the local Gaussian distribution fitting energy model are improved and simulated. Furthermore, from the development of computer graphics and image processing technology to analyze some methods and means of training this professional talent. The experimental results of this article show that the active contour image segmentation algorithm can not only ensure the image segmentation algorithm but also reduce the number of iterations and shorten the image segmentation time. Compared with the CV, LBF, and LGIF models computational efficiency of Segmentation method is increased by 9.2 times, 2.64 times, and 1.44 times, respectively.

Automatic speckle region selection for digital image correlation

With the increase in digital image correlation (DIC) applications, it is an important task to realize the fully automatic measurement and provide an automatic deformation measurement sensor. Keeping this in mind, we propose an automatic speckle region selection method for DIC. This method can be divided into three main steps: initial contour selection, active contour computation, and morphological operation. The sum of modulus of the local intensity gradient vector of the local image is used to determine the initial contour, the active contour computation is driven by improved automatic local Gaussian distribution fitting energy, and the morphological closing operation is finally conducted to achieve better robustness. Experimental results show that the root mean squared error of the localization is less than 0.8260 pixels. We further integrate the proposed method into an open-source DIC software, and the fully automatic deformation analysis is realized. The fully automatic deformation measurement will further widen the application scope of DIC, especially in the field of smart robotics and smart city.

Automatic initialization for active contour models based on particle swarm optimization and application to medical images

The active contour models (ACMs) are one of the most widely used techniques in image segmentation, localization and object tracking. Although there are several existing updated versions of ACMs, in which most of the models do not converge to the desired results in images due to complex background and depends on the initial placement of contour. Among the methods based on the level set, the Local Gaussian Distribution Fitting (LGDF) Energy and the Local Binary Fitting (LBF) Energy are successful algorithms, that suffers from image contours and initial position selection, which are the considerable demerits of the models. To overcome these adversities, we present an efficient and socially-inspired population based stochastic algorithm called particle swarm contour search (PSCS) which is the modified version of particle swarm optimization (PSO) algorithm, considered to be one of the most important optimization methods in swarm intelligence. Firstly, we apply smoothing filters on image to remove high intensities noise. Secondly, we utilized the PSCS algorithm to find the dominant points around the object’s boundaries. The PSCS selects some extra points in different parts of the image rather than the required object, such points are removed in our post PSCS step by utilizing different morphological operations. Furthermore, we calculate the center position and radius of the object for initial contour with the help of points generated using PSCS. The experimental outcome of the segmentations indicate that our proposed approach is automatic and fast for its initialization and successfully segment the desire object in medical images.

A novel method for breast mass segmentation: from superpixel to subpixel segmentation

In this paper, an effective method is proposed for breast mass segmentation using a superpixel generation and curve evolution method. The simple linear iterative clustering method and density-based spatial clustering of applications with noise method are applied to generate superpixels in mammograms at first. Thereafter, a region of interesting (ROI) that contains the breast mass is built on the superpixel generation results. Finally, the image patch and the position of the manual labeled seed are used to build the prior knowledge for the level set method driven by the local Gaussian distribution fitting energy and evolve the curve to capture the edge of breast mass in ROI. Experimental results on mammogram data set demonstrate that the proposed method shows superior performance in contrast to some well-known methods in breast mass segmentation.

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.

Interactive GPU active contours for segmenting inhomogeneous objects

We present a segmentation software package primarily targeting medical and biological applications, with a high level of visual feedback and several usability enhancements over existing packages. Specifically, we provide a substantially faster GPU implementation of the local Gaussian distribution fitting energy model, which can segment inhomogeneous objects with poorly defined boundaries as often encountered in biomedical images. We also provide interactive brushes to guide the segmentation process in a semiautomated framework.The speed of our implementation allows us to visualize the active surface in real time with a built-in ray tracer, where users may halt evolution at any time step to correct implausible segmentation by painting new blocking regions or new seeds. Quantitative and qualitative validation is presented, demonstrating the practical efficacy of our interactive elements for a variety of real-world datasets.

A Novel Active Contour Model for Image Segmentation and Bias Correction Using Guided Image Filtering Regularization Term

This paper presents a novel active contour model for image segmentation and bias correction in terms of robustness to initialization and intensity inhomogeneity. In our model, the local image intensities are described by Gaussian distributions with different means and variances. The local Gaussian distribution fitting energy with a new guided image filtering (GIF) regularization is proposed. The new guided image regularization not only considers the spatial information, but also utilizes the local image content. So compared with the traditional algorithms, the proposed model is less sensitive to initialization and converges faster. Comparative experiments show the advantage of the proposed method.

A Variational Level Set Approach Based on Local Entropy for Image Segmentation and Bias Field Correction.

Image segmentation has always been a considerable challenge in image analysis and understanding due to the intensity inhomogeneity, which is also commonly known as bias field. In this paper, we present a novel region-based approach based on local entropy for segmenting images and estimating the bias field simultaneously. Firstly, a local Gaussian distribution fitting (LGDF) energy function is defined as a weighted energy integral, where the weight is local entropy derived from a grey level distribution of local image. The means of this objective function have a multiplicative factor that estimates the bias field in the transformed domain. Then, the bias field prior is fully used. Therefore, our model can estimate the bias field more accurately. Finally, minimization of this energy function with a level set regularization term, image segmentation, and bias field estimation can be achieved. Experiments on images of various modalities demonstrated the superior performance of the proposed method when compared with other state-of-the-art approaches.

An Improved Image Segmentation Algorithm Based on Local Gaussian Distribution Fitting Energy Model

The active contours driven by local Gaussian distribution fitting (LGDF) energy segmentation method can effectively handle brain tumors and other medical image segmentation. But this method needs a manual initial contour and is more sensitive to the position of the initial contour. It may fail to segment the target image when the initial contours are inappropriate. Therefore, an improved method based on LGDF model is proposed. It combines the unsupervised learning algorithm of K-means clustering algorithm with the LGDF segmentation algorithm. First the K-means clustering algorithm is used to get the primary segmentation and make it as the initial contour position of LGDF model instead of a manual one. Then the LGDF model is used for fine segmentation based on the coarse segmentation of clustering algorithm so as to get an accurate segmentation result. The experimental results show that the proposed method could obtain segmentation automatically rather than manually labeling of the initial contour, and satisfied results are obtained with less calculation time.

A local Gaussian distribution fitting energy-based active contour model for image segmentation

Intensity inhomogeneity and the bias field often occur in real-world images, which cause considerable difficulties in image segmentation. This paper presents a local region-based active contour model for segmentation of images with intensity inhomogeneity and simultaneous estimation of the bias field. In our model, the local image intensities and the bias field are described by the Gaussian distributions with different means and variances. A local Gaussian distribution fitting energy functional is defined on the image region, which combines the level set function and the bias field. Then, gradient flow equations and the bias field are derived for energy minimization. Due to the definition of local image intensities and the bias field, the proposed model is able to deal with intensity inhomogeneity and estimate the bias field. Experimental results on real images demonstrate that the proposed model has advantages over the other classical methods.

Automatic method for white matter lesion segmentation based on T1‐fluid‐attenuated inversion recovery images

The authors propose a fast and effective solution for automatic segmentation of white matter lesions by using T1 and fluid‐attenuated inversion recovery (FLAIR) image modalities with no need for manual segmentation and atlas registration. Initially, a brain tissue segmentation method is used to segment the T1 image into cerebrospinal fluid (CSF), grey matter and white matter. Based on the obtained tissue segmentation results, the region of interest (ROI) of the FLAIR image is created by subtracting the CSF from the FLAIR image. Subsequently, the authors calculate the z‐score of the intensities in the ROI and define a threshold to perform a preliminary identification of abnormalities from normal tissues. The abnormalities obtained at this stage are used as the prior knowledge for the modified level‐set technique. The proposed level set method here is applied based on local Gaussian distribution to precisely detect the boundaries of the white matter lesions in the ROI. The level set method based on local Gaussian distribution fitting energy is robust to the intensity inhomogeneity of MR data and therefore capable of precisely extracting the boundaries of white matter lesions. Experimental analysis and quantitative comparisons with the peak‐seeking and state‐of‐the‐art white matter lesion segmentation (WMLS) techniques demonstrate that the algorithm is a stable and effective approach which significantly outperforms other trusted solutions for white matter lesion segmentation.

Compar ison of Level Set-based Active Contour Models on Subcor tical Image Segmentation

In this paper, we have compared three level set-based active contour (LSAC) methods on inhomogeneous MR image segmentation which is known as an important role of brain diseases to diagnosis and treatment in early. MR image is often occurred a problem with similar intensities and weak boundaries which have been causing many segmentation methods. However, LSAC method could be able to segment the targets such as the level set based on the local image fitting energy, the local binary fitting energy, and local Gaussian distribution fitting energy. Our implemented and tested the subcortical image segmentations were the corpus callosum and hippocampus and finally demonstrated their effectiveness. Consequently, the level set based on local Gaussian distribution fitting energy has obtained the best model to accurate and robust for the subcortical image segmentation.

Waterline Mapping and Change Detection of Tangjiashan Dammed Lake After Wenchuan Earthquake From Multitemporal High-Resolution Airborne SAR Imagery

Several dammed lakes caused by landslides and rock avalanches appeared after Wenchuan earthquake. Tangjiashan dammed lake was the largest one among all of them, which resulted in obvious threats to people around. In this paper, multitemporal high-resolution airborne synthetic aperture radar (SAR) images are used to map the waterline of Tangjiashan dammed lake and its change continuously, which is meaningful for flooding assessment and prediction. An approach combining different techniques is proposed for dammed lake segmentation. In this approach, subpatched intensity thresholding segmentation, morphological operators, and local Gaussian distribution fitting energy (LGDF)-based active contours model (ACM) are used in combination to get the final waterline. Flood-affected area is detected and its size is calculated for each segmental result. In addition, the variations of the flood-affected area are also obtained based on the difference of the above-mentioned segmental results. The proposed approach is tested on real SAR imagery acquired from three different dates in May, 2008 with high-temporal resolution in the same area containing Tangjiashan dammed lake. Experimental results demonstrate the effectiveness of the proposed approach.

ACTIVE CONTOURS DRIVEN BY LOCAL GAUSSIAN DISTRIBUTION FITTING ENERGY BASED ON LOCAL ENTROPY

This paper presents a scheme of improvement on the local Gaussian distribution fitting energy (LGDF) model in terms of robustness to initialization and noise. The LGDF energy is redefined as a weighted energy integral. The weights are defined based on local entropy deriving from a gray level distribution of local image, which enables the proposed model to be robust to the initialization. Experimental results prove that the proposed model is more robustness to noise than the original LGDF model, local binary fitting (LBF) model and local image fitting (LIF) model.

Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy

This paper presents a variational level set approach in a multi-phase formulation to segmentation of brain magnetic resonance (MR) images with intensity inhomogeneity. In our model, the local image intensities are characterized by Gaussian distributions with different means and variances. We define a local Gaussian distribution fitting energy with level set functions and local means and variances as variables. The means and variances of local intensities are considered as spatially varying functions. Therefore, our method is able to deal with intensity inhomogeneity without inhomogeneity correction. Our method has been applied to 3T and 7T MR images with promising results.

Active contours driven by local Gaussian distribution fitting energy

This paper presents a new region-based active contour model in a variational level set formulation for image segmentation. In our model, the local image intensities are described by Gaussian distributions with different means and variances. We define a local Gaussian distribution fitting energy with a level set function and local means and variances as variables. The energy minimization is achieved by an interleaved level set evolution and estimation of local intensity means and variances in an iterative process. The means and variances of local intensities are considered as spatially varying functions to handle intensity inhomogeneities and noise of spatially varying strength (e.g. multiplicative noise). In addition, our model is able to distinguish regions with similar intensity means but different variances. This is demonstrated by applying our method on noisy and texture images in which the texture patterns of different regions can be distinguished from the local intensity variance. Comparative experiments show the advantages of the proposed method.