Original Fuzzy C-means Research Articles

Brain MRI Image Analysis and Segmentation using Machine Learning

The brain magnetic resonance imaging (MRI), analysis and segmentation plays one of the crucial roles in medical diagnosis and facilitates in an early detection of diseases in critical medical conditions, Due to the structural complexity and type of the tumor, radiologists are facing difficulties in extracting essential features of the image which are crucial in treating the patient. Therefore, correct, and meaningful segmentation of brain MRI is a challenging task and is required for further processing. This article proposes machine learning based automatic brain MRI segmentation and classification. The pre-processing step is the vital part of the algorithm, where the discrete wavelet transforms (DWT) and median filtering help in identifying and pointing the exact location of the tumor. The preprocessed image is further segmented by an improved original Fuzzy C-means (FCM) clustering technique. The feature extraction and classification is performed by support vector machine (SVM) classifier. It is found that the simulation associated with ground truth data provides better segmentation results in terms of accuracy, sensitivity, and dice coefficient.

LSCEC: Design of a High-efficiency Leaf-image Segmentation & Classification Model via Ensemble Compute Processes

Real-time leaf image segmentation requires consideration of shadow effects, light variations, capture-angle variations, and zoom effects. Designing an efficient segmentation model that is capable of considering these effects is highly complex, and requires context-specific segmentation techniques. Such techniques are computationally complex, and require larger delays, which limits their applicability for real-time use cases. Moreover, such models are highly context-sensitive and require continuous reconfiguration for multiple image types. To overcome these limitations, this text proposes design of a high-efficiency leaf-image segmentation & classification model via ensemble compute processes. The model initially uses saliency maps for background removal, and cascades it with an ensemble of multiple Fuzzy C Means (FCM) segmentation techniques. These include, Enhanced FCM, k FCM, and original FCM, which results in 3 different image sets. Each of these image sets are individually processed to identify green & yellow coloured foreground pixels. The extracted image mask is filled for better segmentation, and resulting image sets are processed for extraction of features. To efficiently represent the images, a set of Colour, Texture & Convolutional feature maps are extracted, which assists in identification of multidomain feature sets. These feature sets are classified by a combination of Naïve Bayes (NB) Multilayer Perceptron (MLP), Logistic Regression (LR), and Support Vector Machine (SVM) based classification engines, which assists in improving efficiency of disease detection for different crop types. Due to this combination, the proposed model is able to achieve 3.5% better classification accuracy, 1.8% faster response, 4.9% higher precision, and 5.5% better recall when compared with state-of-the-art models. Due to which, the proposed model is capable of deployment for a wide variety of real-time use cases.

Optical classification of inland waters based on an improved Fuzzy C-Means method.

Water optical clustering based on water color information is important for many ecological and environmental application studies, both regionally and globally. The fuzzy clustering method avoids the sharp boundaries in type-memberships produced by hard clustering methods, and thus presents its advantages. However, to make good use of the fuzzy clustering methods on water color spectra data sets, the determination of the fuzzifier parameter (m) of FCM (fuzzy c-means) is the key factor. Usually, the m is set to 2 by default. Unfortunately, this method assigned some membership degrees to non-belonging water type, failing to obtain the unitarity of cluster structure in some cases, especially in inland eutrophic water. To overcome this shortcoming, we proposed an improved FCM method (namely FCM-m) for water color spectra classification by optimizing the fuzzifier parameter. We collected an inland data set containing 1280 in situ spectral data and co-measured water quality parameters with a wide range of biogeochemical variability in China. Using FCM-m, seven spectrally distinct water optical clusters on Sentinel-3 OLCI (Ocean and Land Colour Imager) bands were obtained with the optimized fuzzifier (m=1.36), and the well-performed clustering result is assessed by the validated index (Fuzzy Silhouette Index=0.513). Also, the FCM-m-based soft classification framework was successfully applied to the atmospherically corrected OLCI images, which was evaluated by previous case studies. Besides, by testing FCM-m on three coastal and oceanic data sets, we verified that the optimized m should be adjusted based on the data set itself, and in general, the value gradually approaches 1 with the increase of the band number (or dimension). Finally, the effect of the improved method was tested by Chlorophyll-a concentration estimation. The results show that the algorithm------- blending by FCM-m performs better than that by original FCM, which is mainly because the FCM-m reduces the estimation error from non-belonging clusters by a stricter membership value assignation. To sum up, we believe that FCM-m is an adaptive algorithm, whose R codes are available at https://github.com/bishun945, and needs to be tested by more public data sets.

A Multi-Index Fusion Clustering Strategy for Traffic Flow State Identification

This paper attempts to improve the identification accuracy of traffic flow states, and disclose the impacts of different evaluation indices on the identification results. To this end, a multi-index fusion clustering strategy is proposed in this research. Firstly, flow, velocity and occupancy were selected as the evaluation indices. Then, the weights of the three indices were initialized by a group of experts. After that, the objective function of a weight optimization model was set up to maximize the distance between projection centers of samples under different traffic flow states and to minimize the projection variance between samples under the same traffic flow state. The model was solved by the method of Lagrange multipliers, producing the optimal weight combination. Then, the optimal weights were introduced to the fuzzy c-means (FCM) clustering, forming the multi-index fusion clustering method. The results of example analysis show that our method differentiated between traffic flow states more accurately than the original FCM clustering. And the traffic flow identification accuracy improved from 94.0% to 96.6%. This is because the improved method retains most of the original features of the evaluation indices, which further facilitates the accurate clustering of traffic flow states.

Fuzzyc-Means and Cluster Ensemble with Random Projection for Big Data Clustering

Because of its positive effects on dealing with the curse of dimensionality in big data, random projection for dimensionality reduction has become a popular method recently. In this paper, an academic analysis of influences of random projection on the variability of data set and the dependence of dimensions has been proposed. Together with the theoretical analysis, a new fuzzyc-means (FCM) clustering algorithm with random projection has been presented. Empirical results verify that the new algorithm not only preserves the accuracy of original FCM clustering, but also is more efficient than original clustering and clustering with singular value decomposition. At the same time, a new cluster ensemble approach based on FCM clustering with random projection is also proposed. The new aggregation method can efficiently compute the spectral embedding of data with cluster centers based representation which scales linearly with data size. Experimental results reveal the efficiency, effectiveness, and robustness of our algorithm compared to the state-of-the-art methods.

Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging

Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. On a cohort of 50 breast DCE-MRI studies, PrEIm yielded overall better region and boundary-based statistics compared to the original DCE-MR image, FCM, and PCA based image representations. Additionally, SEAC outperformed a hybrid AC applied to both PCA and FCM image representations. Mean dice similarity coefficient (DSC) for SEAC was significantly better (DSC = 0.74 ± 0.21) than FCM+AC (DSC = 0.50 ± 0.32) and similar to PCA+AC (DSC = 0.73 ± 0.22). Boundary-based metrics of mean absolute difference and Hausdorff distance followed the same trends. Of the automated segmentation methods, breast lesion classification based on morphologic features derived from SEAC segmentation using a support vector machine classifier also performed better (AUC = 0.67 ± 0.05; p < 0.05) than FCM+AC (AUC = 0.50 ± 0.07), and PCA+AC (AUC = 0.49 ± 0.07). In this work, we presented SEAC, an accurate, general purpose AC segmentation tool that could be applied to any imaging domain that employs time series data. SE allows for projection of time series data into a PrEIm representation so that every voxel is characterized by the dominant eigenvectors, capturing the global and local time-intensity curve similarities in the data. This PrEIm allows for the calculation of strong tensor gradients and better region statistics than the original image intensities or alternative image representations such as PCA and FCM. The PrEIm also allows for building a more accurate hybrid AC scheme.

Fuzzy clustering of time series gene expression data with cubic-spline

Data clustering techniques have been applied to ex- tract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success. However, due to the diverse structures and intensive noise, there is no reliable clustering approach can be applied to all gene expression data. In this paper, we aim to the feature of high noise and propose a cubic smoothing spline fitted for the time course ex- pression profile, by which noise can be filtered and then groups genes into clusters by applying fuzzy c-means clustering on the resulting splines (FCMS). The discrete values of radius of curvature are used to compute the similarity between spline curves. Results on gene expression data show that the FCMS has better performance than the original fuzzy c-means on reliability and noise robustness.

Integrated use of fuzzy c-means and convex programming for capacitated multi-facility location problem

In this study a fuzzy c-means clustering algorithm based method is proposed for solving a capacitated multi-facility location problem of known demand points which are served from capacitated supply centres. It involves the integrated use of fuzzy c-means and convex programming. In fuzzy c-means, data points are allowed to belong to several clusters with different degrees of membership. This feature is used here to split demands between supply centers. The cluster number is determined by an incremental method that starts with two and designated when capacity of each cluster is sufficient for its demand. Finally, each group of cluster and each model are solved as a single facility location problem. Then each single facility location problem given by fuzzy c-means is solved by convex programming which optimizes transportation cost is used to fine-tune the facility location. Proposed method is applied to several facility location problems from OR library (Osman & Christofides, 1994) and compared with centre of gravity and particle swarm optimization based algorithms. Numerical results of an asphalt producer's real-world data in Turkey are reported. Numerical results show that the proposed approach performs better than using original fuzzy c-means, integrated use of fuzzy c-means and center of gravity methods in terms of transportation costs.

Research of Lithium-Ion Battery Sorting Method Based on Fuzzy C-Means Algorithm

A method that applies Fuzzy C-Means algorithm (FCM) in lithium-ion battery sorting in formation and test system is presented. The original Fuzzy C-Means algorithm is optimized, and weighted index m and cluster number c are analyzed. Through battery sorting experiment, this method is verified to be feasible and practical.

Fuzzy data mining for segmentation and profiling

The paper presents fuzzy data mining for churn behavior analysis in telecom. Fuzzy data mining is used to identify the clusters in the given large dataset using the modified single pass fuzzy clustering algorithm. Subsequently, fuzzy rule induction is used to reveal variables that provide maximal separation between churners and non-churners in different clusters. This study provides evidence against extra resources for handling large data. The preliminary results are encouraging. The quality of clusters and rules derived for classification are almost identical on both the proposed approach and the original Fuzzy C-Means algorithm.

Spatial Models for Fuzzy Clustering

A novel approach to fuzzy clustering for image segmentation is described. The fuzzy C-means objective function is generalized to include a spatial penalty on the membership functions. The penalty term leads to an iterative algorithm that is only slightly different from the original fuzzy C-means algorithm and allows the estimation of spatially smooth membership functions. To determine the strength of the penalty function, a criterion based on cross-validation is employed. The new algorithm is applied to simulated and real magnetic resonance images and is shown to be more robust to noise and other artifacts than competing approaches.