Fuzzy C-means Image Segmentation Research Articles

Magnetic Resonance Features of Acquired Immune Deficiency Syndrome Involving Central Nervous System Diseases by Intelligent Fuzzy C-Means Clustering (FCM) Algorithm.

This study was aimed to explore the application of fuzzy C-means (FCM) algorithm in MR images of acquired immune deficiency syndrome (AIDS) patients. Sixty AIDS patients with central nervous disease were selected as the research object. A method of brain MR image segmentation based on FCM clustering optimization was proposed, and FCM was optimized based on the neighborhood pixel correlation of gray difference. The correlation was introduced into the objective function to obtain more accurate pixel membership and segmentation features of the image. The segmented image can retain the original image information. The proposed algorithm can clearly distinguish gray matter from white matter in images. The average time of image segmentation was 0.142 s, the longest time of level set algorithm was 2.887 s, and the running time of multithreshold algorithm was 1.708 s. FCM algorithm had the shortest running time, and the average time was significantly better than other algorithms (P < 0.05). FCM image segmentation efficiency was above 90%, and patients can clearly display the location of lesions after MRI imaging examination. In summary, FCM algorithm can effectively combine the spatial neighborhood information of the brain image, segment the BRAIN MR image, analyze the characteristics of AIDS patients from different directions, and provide effective treatment for patients.

Factor analysis thermography for defect detection of panel paintings

ABSTRACT Active infrared thermography is an important non-destructive testing method used for revealing defect structures in materials. In many applications, thermographic data processing is necessary to extract defect features from a large number of thermal images. This work proposes to use a factor analysis thermography (FAT) method that automatically extracts defect features from thermograms via exploratory factor analysis, in tandem with a fuzzy c-means (FCM) clustering algorithm to segment the defects and background. By means of factor rotation, factor analysis minimises the complexity of factor loadings and makes the results more interpretable. Consequently, the defect information is extracted while large signal-to-noise ratios are obtained. Employing the FCM image segmentation algorithm on factor loading images reduces the interference of background on human visual detection. Additionally, the parameter selection is emphasised and addressed. Experiments on a panel painting illustrate that the proposed method promotes the accuracy and efficiency of thermographic detection of defects, compared with the popular principal component thermography (PCT) method.

MURAL LINEAR FEATURE ENHANCEMENT ALGORITHM COMBINING SEMI-SUPERVISED IMAGE SEGMENTATION AND DIMENSIONALITY REDUCTION

Abstract. Due to the influence of natural and human factors, the linear features in the murals are partially blurred, which brings great challenges to the digital preservation and virtual restoration of cultural heritage. Taking the advantages of non-invasive measurement as well as the rich image and spectral information of hyperspectral technology, we proposed a linear feature enhancement method by combining semi-supervised superpixel segmentation with block dimension reduction. The main research work includes: (1) The true color composite image was segmented to obtain the label data by using the local spatial information of the superpixel image and the global feature information extracted by fuzzy c-means (FCM) clustering.(2) According to the label data, the preprocessed hyperspectral data were divided into homogeneous regions, whose dimensionality was reduced by principal component analysis (PCA) and kernel principal component analysis (KPCA). (3) The principal component images with the largest gradient after dimensionality reduction were respectively selected and normalized. The optimal principal component images normalized by the block PCA and block KPCA dimensionality reduction algorithms are superimposed to produce the linear feature enhancement images of murals. The hyperspectral images of some murals in Qutan Temple, Qinghai Province, China were used to verify the method. The results show that the spatial information and the spectral information of different pattern areas in the hyperspectral image can be fully used by combining the superpixel FCM image segmentation algorithm with the dimensionality reduction algorithm. of. It can highlight the linear information in the hyperspectral images of fades murals.

CSFCM: An improved fuzzy C-Means image segmentation algorithm using a cooperative approach

Fuzzy c-means (FCM) is one of the most widely used classification algorithms specially in image segmentation. Like any algorithm, FCM has some drawbacks such as the choice of the number of clusters and the cluster’s center initialization. In this work, we propose new approaches to deal with these two drawbacks. We propose for the first problem two approaches. The first proposed approach exploits neural networks and the Xie and Beni index, while the second one exploits the histogram. Concerning the second problem, we propose a new metaheuristics cooperation approach using the Genetic Algorithm (GA), Biogeography Based Algorithm(BBO), and Firefly Algorithm (FA). This cooperation is managed by a multi-agent system allowing to determine automatically the fittest metaheuristics parameters. Finally, we propose to use a histogram-based version of FCM to reduce the execution time of the algorithm. Experimental results show that our proposed approach improves the performance of the basic FCM algorithm and outperforms other methods proposed in the literature.

Classification of high resolution satellite images using spatial constraints-based fuzzy clustering

A spatial constraints-based fuzzy clustering technique is introduced in the paper and the target application is classification of high resolution multispectral satellite images. This fuzzy-C-means (FCM) technique enhances the classification results with the help of a weighted membership function (Wmf). Initially, spatial fuzzy clustering (FC) is used to segment the targeted vegetation areas with the surrounding low vegetation areas, which include the information of spatial constraints (SCs). The performance of the FCM image segmentation is subject to appropriate initialization of Wmf and SC. It is able to evolve directly from the initial segmentation by spatial fuzzy clustering. The controlling parameters in fuzziness of the FCM approach, Wmf and SC, help to estimate the segmented road results, then the Stentiford thinning algorithm is used to estimate the road network from the classified results. Such improvements facilitate FCM method manipulation and lead to segmentation that is more robust. The results confirm its effectiveness for satellite image classification, which extracts useful information in suburban and urban areas. The proposed approach, spatial constraint-based fuzzy clustering with a weighted membership function (SCFCWmf), has been used to extract the information of healthy trees with vegetation and shadows showing elevated features in satellite images. The performance values of quality assessment parameters show a good degree of accuracy for segmented roads using the proposed hybrid SCFCWmf-MO (morphological operations) approach which also occluded nonroad parts.

Fuzzy C-Means Image Segmentation Algorithm Based on Chaotic Simulated Annealing

Considering the problem that the traditional fuzzy c-means (FCM) image segmentation algorithm is often caught in a specific range in local search and fails to get the globally optimal solution, this paper proposed a modified FCM algorithm based on chaotic simulated annealing (CSA). It traverse all the states without repetition within a certain range to calculate the optimal solution. Experimental results show that our method converges more quickly and accurately to the global optimal and proves a promise global optimization method of high adaptability and feasibility.

A Cluster Number Adaptive Fuzzy c-means Algorithm for Image Segmentation

Aiming at partitioning an image into homogeneous and meaningful regions, automatic image segmentation is a fundamental but challenging problem in computer vision. It is well known that Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the FCM-based image segmentation algorithm must be manually estimated to determine cluster number by users. In this paper, we propose a novel cluster number adaptive fuzzy c-means image segmentation algorithm (CNAFCM) for automatically grouping the pixels of an image into different homogeneous regions when the cluster number is not known beforehand. We utilize the Grey Level Co-occurrence Matrix (GLCM) feature extracted at the image block level instead of at the pixel level to estimate the cluster number, which is used as initialization parameter of the following FCM clustering to endow the novel segmentation algorithm adaptively. We cluster image pixels according to their corresponding Gabor feature vectors to improve the compactness of the clusters and form final homogeneous regions. Experimental results show that proposed CNAFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality, in compare with those FCM-based segmentation methods recently proposed in the literature.

Target recognition by Fast Optimal Fuzzy C-Means image segmentation

We propose a novel Fast Optimal Fuzzy C-Means (FOFCM) clustering algorithm to improve target recognition in image processing. FOFCM can find the best clustering number of images by exploiting the characteristics of the given images, and reduce the segmentation time significantly at the same time. Experiments on serials images are employed to demonstrate the performance of FOFCM. The experiment results show that FOFCM has significantly better performance and lower complexity than previously proposed approaches. The correct recognition rate is increased by 29.24%, which is 94.59%. The clustering efficiency is improved by 6–132 times.

A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns

A novel fuzzy C-mean (FCM) algorithm is proposed for use when active or structured light patterns are projected onto a scene. The underlying inhomogeneous illumination intensity due to the point source nature of the projection, surface orientation and curvature has been estimated and its effect on the object segmentation minimized. Firstly, we modified the recursive FCM algorithm to include biased illumination field estimation. New clustering center and fuzzy clustering functions resulted based on the intensity and average intensity of a pixel neighborhood based object function. Finally, a dilation operator was used on the initial segmented image for further refinement. Experimental results showed the proposed method was effective for segmenting images illuminated by patterns containing underlying biased intensity fields. A higher accuracy was obtained than for traditional FCM and thresholding techniques.