Membership Partition Research Articles

Fuzzy Membership Partition Based Effective Hierarchical Agglomerative Flat Clustering Method for High Dimensional Data

Fuzzy Membership Partition Based Effective Hierarchical Agglomerative Flat Clustering Method for High Dimensional Data

Efficient Segmentation Approach for Different Medical Image Modalities

This paper presents a study of the segmentation of medical images. The paper provides a solid introduction to image enhancement along with image segmentation fundamentals. In the first step, the morphological operations are employed to ensure image detail protection and noise-immunity. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, the Fuzzy C-Means (FCM) clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers. The proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster centers. It is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition matrix. Simulation results are performed on different medical image modalities. Ultrasonic (Us), X-ray (Mammogram), Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance (MR) images are the main medical image modalities used in this work. The obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image segmentation. Simulation results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%, 99.71%, 99.83%, 99.85%, and 99.74% for Us, Mammogram, CT, PET, and MRI images, respectively.

G-Image Segmentation: Similarity-Preserving Fuzzy C-Means With Spatial Information Constraint in Wavelet Space

G-images refer to image data defined on irregular graph domains. This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback-Leibler divergence term on membership partition is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a wavelet space, the proposed FCM is performed in this space rather than Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art FCM algorithms. Moreover, it requires less computation than most of them.

An Efficient Segmentation Technique for Different Medical Image Modalities

In this paper a study on the segmentation of the medical image is carried out. Image segmentation is the process of splitting an image into a number of non-overlapped segments (sets of pixels, also known as image objects). The success of image analysis process depends on accuracy of segmentation process, but a successful segmentation of an image is generally a difficult problem. During an image preprocessing operation, the input given is an image and its output is an enhanced high-quality image as per the techniques used. This paper provides a solid introduction to image enhancement along with image segmentation technique fundamentals. Firstly, the local spatial information of the image is combined with fuzzy c-mean by introducing morphological reconstruction operation to ensure noise-immunity and image detail-protection. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, the modification of membership partition depends only on the spatial neighbors of membership partition instead of the distance between pixels within local spatial neighbors and cluster centers. The proposed algorithm is very simple to implement and significantly fast, since it is not necessary to compute the distance between the neighboring pixels and the cluster centers. It is also efficient when dealing with noisy image because of its ability to improve membership partition matrix efficiently. Experimental results performed on different medical image multimodalities illustrate that the proposed algorithm can achieve better results, as well as it requires short time for the image segmentation process.

Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbors and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper. First, the local spatial information of images is incorporated into FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Second, the modification of membership partition, based on the distance between pixels within local spatial neighbors and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of membership partition. Compared with state-of-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster, since it is unnecessary to compute the distance between pixels within local spatial neighbors and clustering centers. In addition, it is efficient for noisy image segmentation because membership filtering are able to improve membership partition matrix efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.

Assimilated Strong Fuzzy C-means in MR Images for Glioblastoma Multiforme

Background/Objectives: In this paper we segment breast and brain Magnetic Resonance Images. Methods/Statistical Analysis: This automated process implemented by a robust Fuzzy C-Means (FCM). This FCM needs novel objective function. This is obtained by performing replacement. The source is original Euclidean distance. Findings: The target is properties of kernel function on feature space. This transformation uses Tsallis entropy. The effective objective functions are minimized. It results in membership partition matrices and successive prototypes with equation. The initial cluster reduces both the running time and computational complexity. The synthetic image with benchmark dataset used to perform initial experimental work. Then it is applied to real breast and brain magnetic resonance image on different region. Conclusion/Improvements: The silhouette method shows better segmentation than existing method.

New Cluster Validation with Input-Output Causality for Context-Based Gk Fuzzy Clustering

In this paper, a cluster validity concept from an unsupervised to a supervised manner is presented. Most cluster validity criterions were established in an unsupervised manner, although many clustering methods performed in supervised and semi-supervised environments that used context information and performance results of the model. Context-based clustering methods can divide the input spaces using context-clustering information that generates an output space through an input-output causality. Furthermore, these methods generate and use the context membership function and partition matrix information. Additionally, supervised clustering learning can obtain superior performance results for clustering, such as in classification accuracy, and prediction error. A cluster validity concept that deals with the characteristics of cluster validities and performance results in a supervised manner is considered. To show the extended possibilities of the proposed concept, it demonstrates three simulations and results in a supervised manner and analyzes the characteristics.

Strong fuzzy c-means in medical image data analysis

This paper presents a robust fuzzy c-means (FCM) for an automatic effective segmentation of breast and brain magnetic resonance images (MRI). This paper obtains novel objective functions for proposed robust fuzzy c-means by replacing original Euclidean distance with properties of kernel function on feature space and using Tsallis entropy. By minimizing the proposed effective objective functions, this paper gets membership partition matrices and equations for successive prototypes. In order to reduce the computational complexity and running time, center initialization algorithm is introduced for initializing the initial cluster center. The initial experimental works have done on synthetic image and benchmark dataset to investigate the effectiveness of proposed, and then the proposed method has been implemented to differentiate the different region of real breast and brain magnetic resonance images. In order to identify the validity of proposed fuzzy c-means methods, segmentation accuracy is computed by using silhouette method. The experimental results show that the proposed method is more capable in segmentation of medical images than existed methods.

A novel fuzzy logic system based on N-version programming

For the consideration of different application systems, modeling the fuzzy logic rule, and deciding the shape of membership functions are very critical issues due to they play key roles in the design of fuzzy logic control system. This paper proposes a novel design methodology of fuzzy logic control system using the neural network and fault-tolerant approaches. The connectionist architecture with the learning capability of neural network and N-version programming development of a fault-tolerant technique are implemented in the proposed fuzzy logic control system. In other words, this research involves the modeling of parameterized membership functions and the partition of fuzzy linguistic variables using neural networks trained by the unsupervised learning algorithms. Based on the self-organizing algorithm, the membership function and partition of fuzzy class are not only derived automatically, but also the preconditions of fuzzy IF-THEN rules are organized. We also provide two examples, pattern recognition and tendency prediction, to demonstrate that the proposed system has a higher computational performance and its parallel architecture supports noise-tolerant capability. This generalized scheme is very satisfactory for pattern recognition and tendency prediction problems.