Spatial Information For Image Segmentation Research Articles

Three-Direction Fusion for Accurate Volumetric Liver and Tumor Segmentation.

Biomedical image segmentation of organs, tissues and lesions has gained increasing attention in clinical treatment planning and navigation, which involves the exploration of two-dimensional (2D) and three-dimensional (3D) contexts in the biomedical image. Compared to 2D methods, 3D methods pay more attention to inter-slice correlations, which offer additional spatial information for image segmentation. An organ or tumor has a 3D structure that can be observed from three directions. Previous studies focus only on the vertical axis, limiting the understanding of the relationship between a tumor and its surrounding tissues. Important information can also be obtained from sagittal and coronal axes. Therefore, spatial information of organs and tumors can be obtained from three directions, i.e. the sagittal, coronal and vertical axes, to understand better the invasion depth of tumor and its relationship with the surrounding tissues. Moreover, the edges of organs and tumors in biomedical image may be blurred. To address these problems, we propose a three-direction fusion volumetric segmentation (TFVS) model for segmenting 3D biomedical images from three perspectives in sagittal, coronal and transverse planes, respectively. We use the dataset of the liver task provided by the Medical Segmentation Decathlon challenge to train our model. The TFVS method demonstrates a competitive performance on the 3D-IRCADB dataset. In addition, the t-test and Wilcoxon signed-rank test are also performed to show the statistical significance of the improvement by the proposed method as compared with the baseline methods. The proposed method is expected to be beneficial in guiding and facilitating clinical diagnosis and treatment.

Mahalanobis Fuzzy C-Means Clustering with Spatial Information for Image Segmentation

A fuzzy C-Means segmentation algorithm can be implemented in an image segmentationbased on the Mahalanobis distance; However, this method only needs to consider the colorspace situation, not the neighborhood system of the image. It was an effective edge detectionprocess unwell performed and generated less accuracy in segmentation results. In this article,we propose a new method for image segmentation with Mahalanobis fuzzy C-means Spatialinformation (MFCMS). The proposed method combines feature space and images of theinformation of the neighborhood (spatial information) to improve the accuracy of the result ofsegmentation on the image. The MFCMS consists of two steps, the histogram threshold modulefor the first step and the MFCMS module for the second step. The Histogram Threshold moduleis used to get the MFCMS initialization conditions for the cluster centroid and the number ofcentroids. Test results show that this method provides better segmentation performance thanclassification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,respectively.

Robust deep kernel-based fuzzy clustering with spatial information for image segmentation

Clustering algorithms with deep neural network has attracted wide attention to scholars. A deep fuzzy K-means clustering algorithm model on adaptive loss function and entropy regularization (DFKM) is proposed by combining automatic encoder and clustering algorithm. Although it introduces adaptive loss function and entropy regularization to improve the robustness of the model, its segmentation effect is not ideal for high noise. The research purpose of this paper is to focus on the anti-noise performance of image segmentation. Therefore, on the basis of DFKM, this paper focus on image segmentation, combine neighborhood median and mean information of current pixel, introduce neighborhood information of membership degree, and extend Euclidean distance to kernel space by using kernel function, propose a dual-neighborhood information constrained deep fuzzy clustering based on kernel function (KDFKMS). A large number of experimental results show that compared with DFKM and classical image segmentation algorithms, this algorithm has stronger anti-noise robustness.

Fuzzy c-means clustering algorithm with deformable spatial information for image segmentation

Fuzzy c-means clustering algorithm with deformable spatial information for image segmentation

Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation

Spatial information is often used to enhance the robustness of traditional fuzzy c-means (FCM) clustering algorithms. Although some recently emerged improvements are remarkable, the computational complexity of these algorithms is high, which may lead to lack of practicability. To address this problem, an efficient variant named the fuzzy clustering algorithm with variable multi-pixel fitting spatial information (FCM-VMF) is presented. First, a fuzzy clustering algorithm with multi-pixel fitting spatial information (FCM-MF) is developed. Specifically, by dividing the input image into several filter windows, the spatial information of all pixels in each filter window can be obtained simultaneously by fitting the pixels in its corresponding neighbourhood window, which enormously reduces the computational complexity. However, the FCM-MF may result in the loss of edge information. Therefore, the FCM-VMF integrates a variable window strategy with FCM-MF. In this strategy, to preserve more edge information, the sizes of the filter window and generalized neighbourhood window are adaptively reduced. The experimental results show that FCM-VMF is as effective as some recent algorithms. Notably, the FCM-VMF has extremely high efficiency, which means it has a better prospect of application.

Adaptive entropy weighted picture fuzzy clustering algorithm with spatial information for image segmentation

Image segmentation has been broadly applied in computer vision and image analysis. However, many segmentation methods suffer from limited accuracy for noisy images. To improve the robustness of the existing picture fuzzy clustering and solve the problem of selecting spatial constraint parameter, a novel picture fuzzy clustering is proposed. Firstly, a novel symmetric regularizing term is constructed to solve the time-consuming problem of existing picture fuzzy clustering, and the corresponding fuzzy clustering is proposed. Secondly, considering the correlation between current pixel and its neighboring pixels, the objective function is modified by adaptive weighting fusion of local mean information, and the maximum weight entropy constraint is embedded into it to solve the difficulty of parameter selection. Finally, the local spatial information constraint item of the current pixel is constructed by using its neighboring picture fuzzy partition information and is utilized to modify the picture fuzzy partition information of current pixel to correct the clustering center. Results show the proposed algorithm has some potential advantages in segmentation accuracy and anti-noise robustness.

Gamma correction FCM algorithm with conditional spatial information for image segmentation

Gamma correction FCM algorithm with conditional spatial information for image segmentation

Intuitionistic fuzzy set approach to multi-objective evolutionary clustering with multiple spatial information for image segmentation

Abstract Intuitionistic fuzzy set is a useful tool to handle the uncertainty in data. In order to deal with the uncertainty in an image and meanwhile overcome the sensitivity to image noise, a multi-objective evolutionary intuitionistic fuzzy clustering algorithm with multiple image spatial information (MOEIFC-MSI) is proposed to perform image segmentation in this paper. The important innovations of the method are listed as follows: (1) an intuitionistic fuzzy set of the image is first constructed by using a generalized fuzzy complement function; (2) this intuitionistic fuzzy set of the image is then utilized to compute fitness functions and a fuzzy evaluation index for selecting the optimal solution; (3) two kinds of complementary image spatial information are also introduced into the fitness functions and the fuzzy evaluation index to make the proposed method robust to image noise; (4) a real-coded variable string length technique is utilized to encode the cluster centers to automatically determine the number of clusters. Experimental results on synthetic, Berkeley and magnetic resonance (MR) images show that the proposed method outperforms state-of-the-art methods in noise robustness and segmentation performance.

A modified fuzzy C‐means algorithm using scale control spatial information for MRI image segmentation in the presence of noise

AbstractThe fuzzy C‐means (FCM) algorithm does not fully utilize the spatial information for image segmentation and is sensitive to the presence of noise and intensity inhomogeneity in magnetic resonance imaging (MRI) images. The underlying reason is that using a single fuzzy membership function the FCM algorithm cannot properly represent pattern associations to all clusters. In this paper, we present a modified FCM (mFCM) algorithm by incorporating scale control spatial information for segmentation of MRI images in the presence of high levels of noise and intensity inhomogeneity. The algorithm utilizes scale controlled spatial information from the neighbourhood of each pixel under consideration in the form of a probability function. Using this probability function, a local membership function is introduced for each pixel. Finally, new clustering centre and weighted joint membership functions are introduced based on the local membership and global membership functions. The resulting mFCM algorithm is robust to the noise and intensity inhomogeneity in MRI image data and thereby improves the segmentation results. The experimental results on a synthetic image, four volumes of simulated and one volume of real‐patient MRI brain images show that the mFCM algorithm outperforms k‐means, FCM and some other recently proposed FCM‐based algorithms for image segmentation in terms of qualitative and quantitative studies such as cluster validity functions, segmentation accuracy and tissue segmentation accuracy. Copyright © 2015 John Wiley & Sons, Ltd.

Two Fast and Robust Modified Gaussian Mixture Models Incorporating Local Spatial Information for Image Segmentation

The Gaussian Mixture Model (GMM) with the spatial constraint, e.g. Hidden Markov Random Field (HMRF), has been proven effective for image segmentation. The parameter β in the HMRF model is used to balance between robustness to noise and effectiveness of preserving the detail of the image. In other words, the determination of parameter β is, in fact, noise dependent to some degree. In this paper, we propose a simple and effective algorithm to make the traditional Gaussian Mixture Model more robust to noise, with consideration of the relationship between the local spatial information and the pixel intensity value information. The conditional probability of an image pixel is influenced by the probabilities of pixels in its immediate neighborhood to incorporate the spatial and the intensity information. In this case, the parameter β can be assigned to a small value to preserve image sharpness and detail in non-noise images. Meanwhile, the neighborhood window is used to tolerate the noise for heavy-noised images. Thus, the parameter β is independent of image noise degree in our model. Furthermore, we propose another algorithm for our modified GMM (MGMM) with the simplification of conditional probability computation (MGMM_S). Finally, our algorithm is not limited to GMM --- it is general enough so that it can be applied to other distributions based on the construction of the Finite Mixture Model (FMM) technique. Experimental results of synthetic and real images demonstrate the improved robustness and effectiveness of our approach.

Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation

Suppressed fuzzy c-means clustering algorithm (S-FCM) is one of the most effective fuzzy clustering algorithms. Even if S-FCM has some advantages, some problems exist. First, it is unreasonable to compulsively modify the membership degree values for all the data points in each iteration step of S-FCM. Furthermore, duo to only utilizing the spatial information derived from the pixel’s neighborhood window to guide the process of image segmentation, S-FCM cannot obtain satisfactory segmentation results on images heavily corrupted by noise. This paper proposes an optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation to solve the above drawbacks of S-FCM. Firstly, an optimal-selection-based suppressed strategy is presented to modify the membership degree values for data points. In detail, during each iteration step, all the data points are ranked based on their biggest membership degree values, and then the membership degree values of the top r ranked data points are modified while the membership degree values of the other data points are not changed. In this paper, the parameter r is determined by the golden section method. Secondly, a novel gray level histogram is constructed by using the self-tuning non local spatial information for each pixel, and then fuzzy c-means clustering algorithm with the optimal-selection-based suppressed strategy is executed on this histogram. The self-tuning non local spatial information of a pixel is derived from the pixels with a similar neighborhood configuration to the given pixel and can preserve more information of the image than the spatial information derived from the pixel’s neighborhood window. This method is applied to Berkeley and other real images heavily contaminated by noise. The image segmentation experiments demonstrate the superiority of the proposed method over other fuzzy algorithms.

Fuzzy clustering with non-local information for image segmentation

Fuzzy c-means (FCM) algorithms have been shown effective for image segmentation. A series of enhanced FCM algorithms incorporating spatial information have been developed for reducing the effect of noises. This paper presents a robust FCM algorithm with non-local spatial information for image segmentation, termed as NLFCM. It incorporates two factors: one is the local similarity measure depending on the differences between the central pixel and its neighboring pixels in the image; the other is the non-local similarity measure depended on all pixels whose neighborhood configurations are similar to their neighborhood pixels. Furthermore, an adaptive weight is introduced to control the trade-off between local similarity measure and non-local similarity measure. The experimental results on synthetic images and real images under different types of noises show that the new algorithm is effective, and they are relatively independent to the types of noises.

Fuzzy clustering algorithms with self-tuning non-local spatial information for image segmentation

Due to the limitation of the local spatial information in an image, fuzzy c-means clustering algorithms with the local spatial information cannot obtain the satisfying segmentation performance on the image heavily contaminated by noise. In order to compensate this drawback of the local spatial information, an effective kind of non-local spatial information is extracted from the image in this paper. In the acquisition of non-local spatial information, the filtering degree parameter h is a very crucial parameter and needs to be set appropriately. Instead of using a single h value for all the pixels, the calculation of the adaptive parameter h for each pixel is done by studying the statistical characteristics in its search window. Therefore, the non-local spatial information obtained by using the adaptive h value for each pixel is called self-tuning non-local spatial information. In this paper, two novel fuzzy clustering algorithms using the self-tuning non-local spatial information are proposed. In the first algorithm framework, a spatial constraint term by utilizing the self-tuning non-local spatial information for each pixel is defined and then introduced into the objective function of FCM. This algorithm is called fuzzy c-means clustering algorithm with self-tuning non-local spatial information (FCM_SNLS). In the second algorithm framework, a novel gray level histogram is constructed by using the self-tuning non-local spatial information for each pixel, and then clustering is performed on this gray level histogram. This algorithm is called fast fuzzy c-means clustering algorithm with self-tuning non-local spatial information (FFCM_SNLS). Experimental results show that these two proposed methods are not only more effective than fuzzy clustering algorithms with the local spatial information in noise suppression and edge preservation, but also more robust than fuzzy clustering algorithms with the non-local spatial information.

Kernel generalized fuzzy c-means clustering with spatial information for image segmentation

The generalized fuzzy c-means clustering algorithm with improved fuzzy partition (GFCM) is a novel modified version of the fuzzy c-means clustering algorithm (FCM). GFCM under appropriate parameters can converge more rapidly than FCM. However, it is found that GFCM is sensitive to noise in gray images. In order to overcome [email protected]?s sensitivity to noise in the image, a kernel version of GFCM with spatial information is proposed. In this method, first a term about the spatial constraints derived from the image is introduced into the objective function of GFCM, and then the kernel induced distance is adopted to substitute the Euclidean distance in the new objective function. Experimental results show that the proposed method behaves well in segmentation performance and convergence speed for gray images corrupted by noise.

Fuzzy spectral clustering with robust spatial information for image segmentation

In recent years, spectral clustering has become one of the most popular clustering algorithms in areas of pattern analysis and recognition. This algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data, and is simple to implement. However, when the image is corrupted by noise, spectral clustering cannot obtain satisfying segmentation performance. In order to overcome the noise sensitivity of the standard spectral clustering algorithm, a novel fuzzy spectral clustering algorithm with robust spatial information for image segmentation (FSC_RS) is proposed in this paper. Firstly, a non-local-weighted sum image of the original image is generated by utilizing the pixels with a similar configuration of each pixel. Then a robust gray-based fuzzy similarity measure is defined by using the fuzzy membership values among gray values in the new generated image. Thus, the similarity matrix obtained by this measure is only dependent on the number of the gray-levels and can be easily stored. Finally, the spectral graph partitioning method can be applied to this similarity matrix to group the gray values of the new generated image and then the corresponding pixels in the image are reclassified to obtain the final segmentation result. Some segmentation experiments on synthetic and real images show that the proposed method outperforms traditional spectral clustering methods and spatial fuzzy clustering in efficiency and robustness.

A Modified Fuzzy C-Means Clustering with Spatial Information for Image Segmentation

A traditional approach to segmentation of magnetic resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. However, the conventionally standard FCM algorithm is sensitive to noise. To overcome the above problem, a modified FCM algorithm (called MS-FCM later) for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster. In The proposed algorithm every point of the data set has a weight in relation to every cluster. Therefore this weight permits to have a better classification especially in the case of noise data. The proposed algorithm is applied to both artificial synthesized image and real image. Segmentation results demonstrate that the presented algorithm performs more robust to noise than the standard FCM algorithm.

A Novel Kernel-based Fuzzy c-means Algorithm with Spatial Information for Image Segmentation

A Novel Kernel-based Fuzzy c-means Algorithm with Spatial Information for Image Segmentation

Fuzzy c-means clustering with spatial information for image segmentation

A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information.