The SAR image segmentation superpixel-based with optimized spatial information

The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.

Fuzzy C-Means (FCM) clustering is the most well-known clustering method according to fuzzy partition for pattern classification. However, there are some disadvantages of using that clustering method, such as computational complexity and execution time. Therefore, to solve these drawbacks of FCM, the two-phase FCM procedure has been proposed in this study. Compared with the conventional FCM, the usage of a compromised learning scheme makes more adaptive and effective. By performing the proposed approach, the unknown data could be rapidly clustered according to the previous information. A synthetic data set with two dimensional variables is generated to estimate the performance of the proposed method, and to further demonstrate that our method not only reduces computational complexity but economizes execution time compared with the conventional FCM in each example.

An image segmentation method based on optimized spatial texture information is proposed in this article. Spatial information, including the relative position of neighbouring pixels and texture features of the multiscale neighbourhood, is incorporated into the similarity measure of the fuzzy c-means (FCM) clustering algorithm, in which the Gaussian kernel is adopted to diminish the local incorrect segmentation. The FCM clustering is spatially adjusted and optimized by the particle swarm optimization (PSO) algorithm. The purpose of optimization is to obtain the appropriate control parameters influencing spatial information, which can improve segmentation results. Experimental results demonstrate that the proposed method achieves better segmentation performance and is capable of effectively segmenting synthetic images and synthetic aperture radar (SAR) images.

In order to overcome the poor anti-noise performance of traditional fuzzy C-Means (FCM) algorithm in image segmentation, a novel improved FCM algorithm was proposed in this paper based on Particle Swarm Optimization (PSO) algorithm and Markov Random Field (MRF) model, which can make full use of the global searching ability of PSO and the spatial information integrating ability of MRF for image segmentation. In this algorithm, the image segmentation is converted to a PSO optimization problem, in which the fitness function is set up to containing the spatial information based on the spectral value and the neighboring pixels modeled by MRFs. And segmentation results can be iteratively obtained during the PSO iterations according to the newly designed membership function of FCM in which the spatial information is integrated. The experiments herein reported in this paper illustrate the better performance of this algorithm than the traditional FCM algorithm and the PSO algorithm for image segmentation.

Fuzzy c-means is one of the popular clustering technique which has been utilized for medical image analysis. Intuitionistic fuzzy set theory based clustering is an extension of fuzzy c-means which is used for medical image segmentation due to its promising nature for handling the vagueness and uncertainty. The performance of image segmentation is not good in the presence of noise. Many fuzzy and intuitionistic fuzzy set theory based clustering methods have been reported in the literature to handle noise in the segmentation process. In the process of handling noise, most of these methods use smoothing which ignores the important structural information (such as edges and other fine details). In this research work to address this issue, the optimization problem of the proposed IFCM with spatial neighborhood information (IFCMSNI) method is formulated with a novel spatial regularization term which is based on the neighborhood membership value with the advantage of both the intuitionistic fuzzy set theory and a spatial regularization term to handle noise associated with medical images. In the proposed method, the image is represented in the form of Intuitionistic Fuzzy Sets (IFSs) using Sugeno’s negation function. In order to validate the effectiveness of the proposed method, experiments have been carried out on a synthetic image dataset and two publicly available human brain MRI dataset. The segmentation performance of the proposed method is compared with FCM, IFCM, FCMS, FLICM and IIFCM methods in terms of dice score and average segmentation accuracy. The experimental finding endorses the proposed method for image segmentation.

Fuzzy c-means clustering with spatial information for image segmentation

Image segmentation plays a preliminary and indispensable step in medical image processing. Image segmentation plays a crucial role in many medical imaging applications. In this paper, we present a novel algorithm called image Filtering spatial Kernel Fuzzy C-Means(FKFCM) for fuzzy segmentation of magnetic resonance imaging (MRI)data. The algorithm is realized by modifying the objective function in the conventional Fuzzy C-Means (FCM) algorithm using a kernel-induced distance metric, a spatial penalty on the membership functions and then using the image filtering method to correct the image. The algorithm results are compared with standard FCM, Kerneled Fuzzy C-Means (KFCM) and Spatial Fuzzy C-Means(SFCM). The performance of the proposed segmentation algorithm FKFCM provides satisfactory results compared with other algorithms.

Aiming at the problem that traditional fuzzy C-means (FCM) clustering algorithm is susceptible to noise in processing noisy images, a noisy image segmentation method based on FCM wavelet domain feature enhancement is proposed. Firstly, the noise image is decomposed by two-dimensional wavelet. Secondly, the edge enhancement of the approximate coefficient is carried out, and the artificial fish swarm (AFS) optimization algorithm is used to process the threshold value of the detail coefficient, and the processed coefficient is reconstructed by wavelet transform. Finally, the reconstructed image is segmented by FCM algorithm. Five typical gray-scale images are selected by adding Gaussian noise and Salt& pepper noise, respectively, and segmented by various methods. The peak signal-to-noise ratio (PSNR) and error rate (MR) of segmented images are used as performance indexes. Experimental results show that compared with traditional FCM clustering algorithm segmentation method, particle swarm optimization (PSO) segmentation method and other methods, the indexes of image segmentation by the proposed method is greatly improved. It can be seen that the proposed segmentation method retains the texture information of image edge well, and its anti-noise performance and segmentation performance are improved.

Segmentation of brain magnetic resonance imaging (MRI) data plays an important role in the computer-aided diagnosis and neuroscience research. Fuzzy c-means (FCM) clustering algorithm is one of the most usually used techniques for brain MRI image segmentation because of its fuzzy nature. However, the conventional FCM method fails to carry out segmentation well enough due to intensity inhomogeneity in MRI data. To overcome this issue, we propose an improved algorithm based on FCM clustering for segmentation of brain MRI data. Specifically, we modify the conventional FCM algorithm to allow for intensity inhomogeneity by introducing the regularization of the neighborhood influence and bias field. Results show that our proposed algorithm obtains reasonable segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) from MRI data, which is superior to the expectation-maximization (EM) and conventional FCM methods.

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The fuzzy C-means (FCM) clustering is a classical method widely used in image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM method based on the spatial information is proposed for IR ship target segmentation. The improvements include two parts: 1) adding the nonlocal spatial information based on the ship target and 2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov random field. In addition, the results of K -means are used to initialize the improved FCM method. Experimental results show that the improved method is effective and performs better than the existing methods, including the existing FCM methods, for segmentation of the IR ship images.

The fuzzy C-means (FCM) algorithm has got significant importance compared to other methods in medical image segmentation. In this paper, we propose a conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm. This is achieved through the incorporation of conditioning effects imposed by some auxiliary variables and spatial information in the membership functions. By combining these two aspects, we are able to solve the problems of sensitivity to noisy data and inhomogeneity. The experimental results on several simulated and real-patient MRI brain images show that the csFCM method has superior performance on image segmentation than the FCM algorithm and some other FCM-based clustering algorithms.

Image segmentation has key influence in numerous medical imaging uses. In this paper, we present a new algorithm for spatial fuzzy segmentation using modified particle swarm optimization of medical & multimedia data. The algorithm is realized by modifying the scaling parameters in the conventional fuzzy C-means (FCM) algorithm using Modified Particle Swarm Optimization (MPSO). Spatial coordinates depend on filter radius, weight, gradient & threshold. Firstly, the initial parameters in the FCM are replaced by optimized parameters, & thus the corresponding algorithm is derived & called as the SFCM-MPSO. Outcomes of the experiment on both real MR images & Multimedia images demonstrate that the proposed algorithms have better performance when noise & other artefacts are present than the standard algorithms. Voxel based error method shows Positive Predictive Value (PPV), Sensitivity (Sens), & Dice Similarity Coefficient (DSC) between existing methodology.

Fuzzy C-means (FCM) clustering method has been widely used in image segmentation that plays an important role in a variety of applications in image processing and computer vision systems, but the performance of FCM heavily relies on the initial cluster centers which are difficult to determine. To solve the problem, the paper proposes a new hybrid method for image segmentation, which first randomly generates a population of initial clustering solutions and then uses an evolutionary algorithm to search for better clustering solutions; at each iteration, the FCM method is applied on each initial clustering solution to produce its segmentation result. Among a set of popular evolutionary algorithms, we find that the biogeography-based optimization (BBO) metaheuristic exhibits good performance on the considered problem. Besides the basic BBO, we have also proposed a set of improved BBO versions in their combination with FCM for image segmentation. Computational experiments on a set of test images show that the proposed method has significant advantage over the basic FCM algorithm and those hybrid algorithms combining FCM with other evolutionary algorithms such as artificial bee colony and particle swarm optimization.

Abstr act Image segmentation is the foundation of computer vision and pattern recognition but is still a challenging problem. FCM has been used in image segmentation many years but its problem that is sensitive to noise still cannot be solved effectively. This paper proposes an improved algorithm that incorporates the spatial information and classification modification into the fuzzy C-means (FCM), which can overcome this shortcoming of the traditional FCM without increasing the computational complexity. In this algorithm, FCM will be used for initial segmentation, and then the classification of the element will be modified according to the dispersion of the class. After that, selective smoothing will be executed, which changes the gray value of the pixel according to the class its neighboring elements belong to. Finally, the image treated will be segregated again using FCM. As the experimental results shows, this algorithm can suppress noise efficiently and even can be used in image denoising. Moreover, the segmentation result of the uneven illumination image is also nice.

Bones are a common site of metastases in a number of cancers including prostate and breast cancer. Assessing response or progression typically relies on planar bone scintigraphy. However, quantitative bone SPECT (BQSPECT) has the potential to provide more accurate assessment. An important component of BQSPECT is segmenting lesions and bones in order to calculate metrics like tumor uptake and metabolic tumor burden. However, due to the poor spatial resolution, noise, and contrast properties of SPECT images, segmentation of bone SPECT images is challenging. In this study, we propose and evaluate a fuzzy C-means (FCM) clustering based semi-automatic segmentation method on quantitative Tc-99m MDP quantitative SPECT/CT. The FCM clustering algorithm has been widely used in medical image segmentation. Yet, the poor resolution and noise properties of SPECT images result in sub-optimal segmentation. We propose to incorporate information from registered CT images, which can be used to segment normal bones quite readily, into the FCM segmentation algorithm. The proposed method modifies the objective function of the robust fuzzy C-means (RFCM) method to include prior information about bone from CT images and spatial information from the SPECT image to allow for simultaneously segmenting lesion and bone in BQSPECT/CT images. The method was evaluated using realistic simulated BQSPECT images. The method and algorithm parameters were evaluated with respect to the dice similarity coefficient (DSC) computed using segmentation results. The effect of the number of iterations used to reconstruct the BQSPECT images was also studied. For the simulated BQSPECT images studied, an average DSC value of 0.75 was obtained for lesions larger than 2 cm3 with the proposed method.