Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images
01 Jan 2017-Journal of Biomimetics, Biomaterials and Biomedical Engineering (Trans Tech Publications Ltd)-Vol. 30, pp 12-23
TL;DR: It was concluded that the possibilistic exponential fuzzy c-means segmentation algorithm endorsed for additional efficient for accurate detection of breast tumours to assist for the early detection.
Abstract: Different fuzzy segmentation methods were used in medical imaging from last two decades for obtaining better accuracy in various approaches like detecting tumours etc. Well-known fuzzy segmentations like fuzzy c-means (FCM) assign data to every cluster but that is not realistic in few circumstances. Our paper proposes a novel possibilistic exponential fuzzy c-means (PEFCM) clustering algorithm for segmenting medical images. This new clustering algorithm technology can maintain the advantages of a possibilistic fuzzy c-means (PFCM) and exponential fuzzy c-mean (EFCM) clustering algorithms to maximize benefits and reduce noise/outlier influences. In our proposed hybrid possibilistic exponential fuzzy c-mean segmentation approach, exponential FCM intention functions are recalculated and that select data into the clusters. Traditional FCM clustering process cannot handle noise and outliers so we require being added in clusters due to the reasons of common probabilistic constraints which give the total of membership’s degree in every cluster to be 1. We revise possibilistic exponential fuzzy clustering (PEFCM) which hybridize possibilistic method over exponential fuzzy c-mean segmentation and this proposed idea partition the data filters noisy data or detects them as outliers. Our result analysis by PEFCM segmentation attains an average accuracy of 97.4% compared with existing algorithms. It was concluded that the possibilistic exponential fuzzy c-means segmentation algorithm endorsed for additional efficient for accurate detection of breast tumours to assist for the early detection.
Citations
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TL;DR: A method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy.
Abstract: In this paper, a method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the K-Means clustering algorithm (FCM-KM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-KM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification.
137 citations
Cites methods from "Clustering Algorithm in Possibilist..."
...With the characteristics of sensitivity and ergodicity, a dynamic population firefly algorithm (FCM algorithm) based on the chaos theory as well as the maximum and minimum distance algorithm is adopted to improve K-means clustering [36]–[41]....
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TL;DR: It is indicated that the Spark-based parallel FCM algorithm provides faster speed of segmentation for agricultural image big data and has better scale-up and size-up rates.
Abstract: With the explosive growth of image big data in the agriculture field, image segmentation algorithms are confronted with unprecedented challenges. As one of the most important images segmentation technologies, the fuzzy c-means (FCMs) algorithm has been widely used in the field of agricultural image segmentation as it provides simple computation and high-quality segmentation. However, due to its large amount of computation, the sequential FCM algorithm is too slow to finish the segmentation task within an acceptable time. This paper proposes a parallel FCM segmentation algorithm based on the distributed memory computing platform Apache Spark for agricultural image big data. The input image is first converted from the RGB color space to the lab color space and generates point cloud data. Then, point cloud data are partitioned and stored in different computing nodes, in which the membership degrees of pixel points to different cluster centers are calculated and the cluster centers are updated iteratively in a data-parallel form until the stopping condition is satisfied. Finally, point cloud data are restored after clustering for reconstructing the segmented image. On the Spark platform, the performance of the parallel FCMs algorithm is evaluated and reaches an average speedup of 12.54 on ten computing nodes. The experimental results show that the Spark-based parallel FCMs algorithm can obtain a significant increase in speedup, and the agricultural image testing set delivers a better performance improvement of 128% than the Hadoop-based approach. This paper indicates that the Spark-based parallel FCM algorithm provides faster speed of segmentation for agricultural image big data and has better scale-up and size-up rates.
41 citations
Cites background from "Clustering Algorithm in Possibilist..."
...Although recent researches have proposed novel FCM algorithms for improving the computational efficiency [23], the cluster process is still a computationintensive task, where the membership degree must be computed on each pixel point....
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TL;DR: Experimental results showed that the SLR improves the classification performance of AD/MCI compared other classical methods and introduces sparse logistic regression for the early diagnosis of AD.
Abstract: Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI) are critical for the effective treatment of AD. However, compared with AD classification tasks, predicting the conversion of MCI to AD is relatively difficult. as there are only minor differences among MCI groups. What’s more, in brain imaging analysis, the high dimensionality and relatively small number of subjects brings challenges to computer-aided diagnosis of AD and MCI. Many previous researches focused on the identification of imaging biomarkers for AD diagnosis. In this paper, we introduce sparse logistic regression for the early diagnosis of AD. Sparse logistic regression (SLR) uses L1/2 regularization to impose a sparsity constraint on logistic regression. The L1/2 regularization is considered a representative of Lq regularization, where fewer but informative key brain regions are applied for the classification of AD/MCI. We evaluated the SLR on 197 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results showed that the SLR improves the classification performance of AD/MCI compared other classical methods.
26 citations
TL;DR: An improved weighted least-square algorithm based on an enhanced non-naive Bayesian classifier (ENNBC) method that can reduce the root-mean-squared error of the position compared with the extended Kalman filter and has better robustness against large localization and tracking errors.
Abstract: The outliers remove, the classification of effective measurements, and the weighted optimization method of the corresponding measurement are the main factors that affect the positioning accuracy based on range-based multi-target tracking in wireless sensor networks. In this paper, we develop an improved weighted least-square algorithm based on an enhanced non-naive Bayesian classifier (ENNBC) method. According to the ENNBC method, the outliers in the measurement data are removed effectively, dataset density peaks are found quickly, and remaining effective measurements are accurately classified. The ENNBC method improves the traditional direct classification method and took the dependence among continuous density attributes into account. Four common indexes of classifiers are used to evaluate the performance of the nine methods, i.e., the normal naive Bayesian, flexible naive Bayesian (FNB), the homologous model of FNB (FNB
ROT
), support vector machine, k-means, fuzzy c-means (FCM), possibilistic c-means, possibilistic FCM, and our proposed ENNBC. The evaluation results show that ENNBC has the best performance based on the four indexes. Meanwhile, the multi-target tracking experimental results show that the proposed algorithm can reduce the root-mean-squared error of the position compared with the extended Kalman filter. In addition, the proposed algorithm has better robustness against large localization and tracking errors.
17 citations
Cites methods from "Clustering Algorithm in Possibilist..."
...PARAMETER DETERMINATION AND EXPERIMENT RESULTS In this section, the proposed classification algorithm [51] is compared with the NNB, FNB, FNBROT , SVM, k-means, FCM, [52]–[54], PCM, and PFCM [55] on training datasets with respect to the four indexes, i....
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TL;DR: The ROIs obtained using the proposed classifier-based segmentation algorithm was compared with the ground truth annotated by the radiologists and the performance of the proposed algorithm was evaluated.
Abstract: Breast cancer occurs as a result of erratic growth and proliferation cells that originate in the breast. In this paper, the classifiers were used to identify the abnormalities on mammograms to get the region of interest (ROI). Before classifier based segmentation, noise, pectoral muscles, and tags were removed for a successful segmentation process. Then the proposed approach extracted the brightest regions using modified k-means. From the extracted brightest regions, shape and texture features were extracted and given to classifiers (KNN and SVM) and marked as ROI only those non-overlapping abnormal regions. The ROIs obtained using the proposed classifier-based segmentation algorithm was compared with the ground truth annotated by the radiologists. The datasets used to evaluate the performance of the proposed algorithm was public (MIAS) and local datasets (BGH and DADC).
16 citations
Cites methods from "Clustering Algorithm in Possibilist..."
...For reducing the number of iterations in PFCM, intuitionistic fuzzy c-mean was adopted in [17, 18]....
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References
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TL;DR: An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
Abstract: The clustering problem is cast in the framework of possibility theory. The approach differs from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values can be interpreted as degrees of possibility of the points belonging to the classes, i.e., the compatibilities of the points with the class prototypes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function. The advantages of the resulting family of possibilistic algorithms are illustrated by several examples. >
2,388 citations
"Clustering Algorithm in Possibilist..." refers methods in this paper
...For updating membership equations, proper conditions are derived to use the identical approach as Krishnapuram [19]....
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...[19] R. Krishnapuram, J.M. Keller, A possibilistic approach to clustering, IEEE Trans....
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...In the case of varied values, it may leads to instabilities as found by Krishnapuram [19]....
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...For updating membership equations, proper conditions are derived to use the identical approach as Krishnapuram [19]. pqµ are autonomous of every one....
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TL;DR: A new model called possibilistic-fuzzy c-means (PFCM) model, which solves the noise sensitivity defect of FCM, overcomes the coincident clusters problem of PCM and eliminates the row sum constraints of FPCM.
Abstract: In 1997, we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM constrains the typicality values so that the sum over all data points of typicalities to a cluster is one. The row sum constraint produces unrealistic typicality values for large data sets. In this paper, we propose a new model called possibilistic-fuzzy c-means (PFCM) model. PFCM produces memberships and possibilities simultaneously, along with the usual point prototypes or cluster centers for each cluster. PFCM is a hybridization of possibilistic c-means (PCM) and fuzzy c-means (FCM) that often avoids various problems of PCM, FCM and FPCM. PFCM solves the noise sensitivity defect of FCM, overcomes the coincident clusters problem of PCM and eliminates the row sum constraints of FPCM. We derive the first-order necessary conditions for extrema of the PFCM objective function, and use them as the basis for a standard alternating optimization approach to finding local minima of the PFCM objective functional. Several numerical examples are given that compare FCM and PCM to PFCM. Our examples show that PFCM compares favorably to both of the previous models. Since PFCM prototypes are less sensitive to outliers and can avoid coincident clusters, PFCM is a strong candidate for fuzzy rule-based system identification.
1,118 citations
"Clustering Algorithm in Possibilist..." refers methods in this paper
...For resolution of these drawbacks, it is useful to go through another approach which combines possibilistic fuzzy clustering (PFCM) with FCM [18]....
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...1 1 1 1 ( 1 ). k N k N n n pq pq q pq q p q p PFCM dµ λ µ = = = = = ∑ ∑ + ∑ ∑ − (8) Optimal solution Eq....
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...This PFCM function can be represented as in Eq....
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...The possibilistic method was incorporated addicted to FCM for relaxing such condition to overcome the above situations and called as possibilistic fuzzy clustering (PFCM) [1,11,12]....
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01 Jul 1997
TL;DR: F fuzzy-possibilistic c-means is proposed, and it is shown that FPCM solves the noise sensitivity defect of FCM, and also overcomes the coincident clusters problem of PCM.
Abstract: We justify the need for computing both membership and typicality values when clustering unlabeled data. Then we propose a new model called fuzzy-possibilistic c-means (FPCM). Unlike the fuzzy and possibilistic c-means (FCM/PCM) models, FPCM simultaneously produces both memberships and possibilities, along with the usual point prototypes or cluster centers for each cluster We show that FPCM solves the noise sensitivity defect of FCM, and also overcomes the coincident clusters problem of PCM. Then we derive first order necessary conditions for extrema of the PFCM objective function, and use them as the basis for a standard alternating optimization approach to finding local minima. Three numerical examples are given that compare FCM to FPCM. Our calculations show that FPCM compares favorably to FCM.
344 citations
"Clustering Algorithm in Possibilist..." refers methods in this paper
...For resolution of these drawbacks, it is useful to go through another approach which combines possibilistic fuzzy clustering (PFCM) with FCM [18]....
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...Fuzzy cmean (FCM) include data in every cluster with the separate degree of membership [4,10,18]....
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01 Mar 2011
TL;DR: Experimental results show the effectiveness of the proposed method in contrast to conventional fuzzy C means algorithms and also type II fuzzy algorithm.
Abstract: This paper presents a novel intuitionistic fuzzy C means clustering method using intuitionistic fuzzy set theory. The intuitionistic fuzzy set theory considers another uncertainty parameter which is the hesitation degree that arises while defining the membership function and thus the cluster centers may converge to a desirable location than the cluster centers obtained using fuzzy C means algorithm. Also a new objective function which is the intuitionistic fuzzy entropy is incorporated in the conventional fuzzy C means clustering algorithm. This is done to maximize the good points in the class. This clustering method is used in clustering different regions of the CT scan brain images and these may be used to identify the abnormalities in the brain. Experimental results show the effectiveness of the proposed method in contrast to conventional fuzzy C means algorithms and also type II fuzzy algorithm.
334 citations
01 Sep 2016
TL;DR: A novel approach is presented, named an improved intuitionistic fuzzy c-means (IIFCM), which considers the local spatial information in an intuitionists fuzzy way, which preserves the image details, is insensitive to noise, and is free of requirement of any parameter tuning.
Abstract: Original and segmented simulated brain image by different algorithms: (a) axial view of original simulated T1-weighted brain image with INU=0 and 1% noise, (b) skull stripping simulated brain image, (c) manual segmented CSF, GM and WM images, (d) IIFCM algorithm, (e) IFCM algorithm, (f) FLICM algorithm, (g) EnFCM algorithm, (h) FGFCM algorithm, (i) FCM_S1 algorithm, (j) FCM_S2 algorithm, (k) ImFCM algorithm. The segmentation of brain magnetic resonance (MR) images plays an important role in the computer-aided diagnosis and clinical research. However, due to presence of noise and uncertainty on the boundary between different tissues in the brain image, the segmentation of brain image is a challenging task. Many variants of standard fuzzy c-means (FCM) algorithm have been proposed to handle the noise. Intuitionistic fuzzy c-means (IFCM) algorithm, one of the variants of FCM, is found suitable for image segmentation. It incorporates the advantage of intuitionistic fuzzy sets theory. The IFCM successfully handles the uncertainty but it is sensitive to noise as it does not incorporate any local spatial information. In this paper, we have presented a novel approach, named an improved intuitionistic fuzzy c-means (IIFCM), which considers the local spatial information in an intuitionistic fuzzy way. The IIFCM preserves the image details, is insensitive to noise, and is free of requirement of any parameter tuning. The obtained segmentation results on synthetic square image, real and simulated MRI brain image demonstrate the efficacy of the IIFCM algorithm and superior performance in comparison to existing segmentation methods. A nonparametric statistical analysis is also carried out to show the significant performance of the IIFCM algorithm in comparison to other existing segmentation algorithms.
147 citations
Additional excerpts
...FCM is being the basic and main classical fuzzy clustering approach [2,13,14]....
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