Fuzzy C-means Clustering Algorithm Research Articles

Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor.

The guided filter is a novel explicit image filtering method, which implements a smoothing filter on “flat patch” regions and ensures edge preserving on “high variance” regions. Recently, the guided filter has been successfully incorporated into the process of fuzzy c-means (FCM) to boost the clustering results of noisy images. However, the adaptability of the existing guided filter-based FCM methods to different images is deteriorated, as the factor ε of the guided filter is fixed to a scalar. To solve this issue, this paper proposes a new guided filter-based FCM method (IFCM_GF), in which the guidance image of the guided filter is adjusted by a newly defined influence factor ρ. By dynamically changing the impact factor ρ, the IFCM_GF acquires excellent segmentation results on various noisy images. Furthermore, to promote the segmentation accuracy of images with heavy noise and simplify the selection of the influence factor ρ, we further propose a morphological reconstruction-based improved FCM clustering algorithm with guided filter (MRIFCM_GF). In this approach, the original noisy image is reconstructed by the morphological reconstruction (MR) before clustering, and the IFCM_GF is performed on the reconstructed image by utilizing the adjusted guidance image. Due to the efficiency of the MR to remove noise, the MRIFCM_GF achieves better segmentation results than the IFCM_GF on images with heavy noise and the selection of the influence factor for the MRIFCM_GF is simple. Experiments demonstrate the effectiveness of the presented methods.

Robust Fuzzy Clustering Algorithm Based on Adaptive Neighbors

In pattern recognition, fuzzy logic is widely used in unsupervised classification or clustering methods. Fuzzy c-means (FCM) clustering algorithm is a typical dynamic clustering algorithm of the fuzzy c-means algorithm based on the error square sum criterion. It introduces fuzzy membership and optimizes the objective function to obtain each sample point for all classes. The membership degree of the center is used to automatically classify the data sample. However, the FCM algorithm is susceptible to the noise points and outliers, and the unbalanced data structure reduces the generalization performance of the algorithm. In this paper, we propose a fuzzy c-means clustering algorithm with adaptive neighbors weight learning. Through adaptive neighborhood robust weight learning, an adaptive weight vector with robustness and sparsity is obtained. During the optimization, we only activate the k samples with the shortest distance to the cluster center and eliminate extreme noise samples to improve the global robustness and sparsity of the algorithm. Finally, empirical analysis verifies the superiority and effectiveness of the proposed clustering method.

Adaptive Neuro-Fuzzy Inference System to Predict the Dynamic Modulus of Hot Mix Asphalt

The dynamic modulus |E*| of hot mix asphalt (HMA) plays a fundamental role in the mechanistic–empirical pavement design. The Witczak regression-based predictive model can be considered as the most fundamental and widely used model to estimate the |E*| of HMA. However, the effect of confining stress has not been considered in this model. In this paper, the feasibility of applying the robust machine learning technique called “adaptive neuro-fuzzy inference system” (ANFIS) was investigated to predict the |E*| of HMA using 1,320 test results performed at the University of Maryland. Asphalt mix parameters, testing frequency, temperature, and the level of confining stress were considered as the model inputs. Also, intercept of temperature susceptibility relationship (A) and slope of temperature susceptibility relationship (VTS). Two new ANFIS models were developed using two different structures, including fuzzy C-mean clustering (FCM) and subtractive clustering (SC) algorithms. In addition, two computer programs were developed for optimizing the structure of the FCM ANFIS and SC ANFIS models to achieve the highest predicting accuracy. The obtained results indicate that the SC and FCM ANFIS models predict the |E*| of HMA with high coefficients of determination of R2=0.948 and 0.945, respectively. Moreover, a sensitivity analysis was conducted to evaluate the level of influence of model inputs, and the results show that the confining stress has a significant impact on the |E*|. Also, the results demonstrate that the standard results for the ANFIS model and real data are in complete agreement.

Anomaly detection based on a granular Markov model

Since time series are characterized by a substantial volume of data, high levels of noise and the correlation between data in the time series attributes, it becomes challenging to mine crucial information from the series and apply it to anomaly detection. In this study, inspired by the concept of information granularity being applied to the process of system modelling, a granular Markov model is proposed for time series anomaly detection. Anomalies are generally caused by the changes in amplitude and shape; in this study we take both the original time series data and their amplitude change data into consideration. First, we utilize an interval information granularity representation based on the principle of justifiable granularity to represent the original time series data in an abstract manner to arrive at the corresponding representation results-- that is, interval information granules. Then, based on the results of the interval information granularity representation and the Fuzzy C-Means (FCM) clustering algorithm, a granular Markov model is developed to produce anomaly scores to quantify possible anomalies. Compared with state-of-the-art methods, experimental studies completed for a large number of datasets demonstrate that the proposed method can significantly improve the anomaly detection process with higher data anomaly resolution. The obtained results are consistent across all datasets.

Mobile Application to Detect Covid-19 Pandemic by Using Classification Techniques: Proposed System

<p class="0abstract">Various mobile applications such as Mobile Health (mHealth) have been developed and spread across the world which has played an important role in mitigating the Coronavirus pandemic (COVID-19). As the COVID-19 pandemic spreads, several people have drawn parallels to influenza. While both viruses cause respiratory infections, they propagate in very different ways. This has a major impact on the public health measures that can be used to fight each virus. These viruses are pandemic-causing in the same way. That is, they both cause respiratory disease, and can present themselves in several ways, ranging from asymptomatic to severe and deadly. A proposal is presented in this paper that uses two algorithms to define and classify these pandemics, they are: The Back Propagation (BP) classification algorithm and the Fuzzy C-Mean (FCM) clustering algorithm. Two stages are implemented in the proposed system: in the first step, the FCM algorithm is used to find out the type of virus, and this algorithm is capable of handling ambiguous features of viruses. In the second step, a BP neural network is used as a classifier to detect the pandemic class. The proposed system was trained and tested using a well-known dataset (covid-19 vs influenza). Information Gain (IG) is used to optimize the related features that affect the classification process to improve speed and accuracy. The proposed mobile application is developed to support users easily detecting the COVID-19 infection by inputting the medical tests as significant features to the proposed system. The proposed system's accuracy is up to (89%), the framework was created using the Matlab programming environment and an Android Studio for Mobil application designing.</p>

Optimal exit choice during highway tunnel evacuations based on the fire locations.

In the case of a fire, the choice of exit in the highway tunnel is strictly limited by fire location, which seriously affects the evacuation time. A spontaneous or disorderly exit choice might result in a decreased evacuation efficiency and utilization rate of exits. In this paper, we propose a strategy to obtain the optimal exit choice based on fire location during highway tunnel evacuations. In our strategy, first, the vehicle distributions and locations of evacuating occupants are determined in the traffic simulation program VISSIM. The evacuation simulation software BuildingEXODUS is employed to obtain the corresponding parameters of the evacuation process and analyze the impacts of different fire locations on the evacuation time. During the analysis, the optimal productivity statistics (OPS) is selected as the evaluation index. Then, the feature points of the crowding occupants are captured by the fuzzy c-means (FCM) cluster algorithm. Next, based on the feature points, the relationship between the location of the fire and boundary of the optimal exit choice under the optimal OPS is obtained through the polynomial regression model. It is found that the R-squared(R2) and sum of squares for error (SSE) of the polynomial regression model, reflecting the accuracy estimation, are 98.02% and 2.79×10-4, respectively. Moreover, different fire locations impact the evacuation time of tunnel entrance and evacuation passageway. This paper shows that the location of the fire and boundary of optimal exit choice have a negative linear correlation. Taking the fire 110 m away from the evacuation passageway as an example, the OPS of our strategy can be decreased by 35.6% when compared with no strategies. Our proposed strategy could be applied to determine the location of variable evacuation signs to help evacuating occupants make optimal exit choices.

Extraction of plateau lake water bodies based on an improved FCM algorithm

Changes in plateau body lake water are an important indicator of global ecosystem changes, and a timely and accurate grasp of this change information can provide a scientific reference for the formulation of relevant policies. The traditional fuzzy C-means clustering (FCM) algorithm takes into account the ambiguity of the classification of the ground object pixels but does not consider the rich spectral information of the neighboring pixels and is very sensitive to the background noise” of the remote sensing image, resulting in low water extraction accuracy. Aiming to compensate for the shortcomings of the traditional FCM algorithm, this paper proposes an improved FCM algorithm. This algorithm replaces the Euclidean distance of the traditional FCM algorithm with a combination of the Mahalanobis distance and spectral angle matching (SAM) to fully take into account the spectral information of neighboring pixels and improve the clustering accuracy. The study selected Sentinel-2 images of the Fuxian Lake and Xingyun Lake basins during normal, wet, and dry periods as the data source. Under the same conditions, the clustering accuracy was compared with the traditional FCM algorithm, improved FCM algorithm, K-means clustering method and iterative self-organizing data analysis (ISODATA) clustering method. The experimental results show that the improved FCM algorithm has a higher water extraction accuracy than the traditional FCM algorithm, K-means clustering method and ISODATA clustering method. The kappa coefficient and overall accuracy (OA) of the improved FCM algorithm can be increased by 5.56%–9.45% and 2.66%–5.32%, respectively, and the omission error and commission error can be reduced by 1.72%–4.55% and 12.14%–22.10%, respectively. When the improved FCM algorithm is used, the extraction accuracy is higher for plateau deep lakes than for plateau shallow lakes, and the extraction effect for lakes with poor water environments is more significant than that of other methods. The improved FCM algorithm better maintains the integrity of the water boundary and overcomes the influence of a certain number of mountain shadows and urban building pixels on the clustering results.

A new model of faults classification in power transformers based on data optimization method

The current paper aims to present a hybrid model for classifying faults in power transformers. The innovation of this paper can be shown by introducing a new method of data optimization based on the combination (subsets) of the set (C-set) method and the unsupervised fuzzy C-means (FCM) clustering algorithm. The new method aims to solve the quandaries of unbalanced data, outliers, and boundary ratios that exist in conventional and artificial methods. Considering the compactness of dissolved gas analysis (DGA) data, the C-set method is employed for dividing the data samples according to their fault types into multiple groups without repetition. The FCM algorithm is then adopted for the samples pre-selection process by obtaining the cluster centers of each C-set class. The obtained cluster centers are combined to form the labeled expert training data (LETD) set, which will train the 1-vs-1 multiclass support vector machine (MCSVM) with a linear kernel. The proposed model diagnosis obtained accuracy is 88.9%. Our proposed model has been compared with other soft computing and traditional models. The experimental results of this model revealed high performance in classifying transformer faults and improving the fault identification accuracy.

Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation

Spatial information is often used to enhance the robustness of traditional fuzzy c-means (FCM) clustering algorithms. Although some recently emerged improvements are remarkable, the computational complexity of these algorithms is high, which may lead to lack of practicability. To address this problem, an efficient variant named the fuzzy clustering algorithm with variable multi-pixel fitting spatial information (FCM-VMF) is presented. First, a fuzzy clustering algorithm with multi-pixel fitting spatial information (FCM-MF) is developed. Specifically, by dividing the input image into several filter windows, the spatial information of all pixels in each filter window can be obtained simultaneously by fitting the pixels in its corresponding neighbourhood window, which enormously reduces the computational complexity. However, the FCM-MF may result in the loss of edge information. Therefore, the FCM-VMF integrates a variable window strategy with FCM-MF. In this strategy, to preserve more edge information, the sizes of the filter window and generalized neighbourhood window are adaptively reduced. The experimental results show that FCM-VMF is as effective as some recent algorithms. Notably, the FCM-VMF has extremely high efficiency, which means it has a better prospect of application.

Improving the emotion‐based classification by exploiting the fuzzy entropy in FCM clustering

Emotion detection in the natural language text has drawn the attention of several scientific communities as well as commercial/marketing companies: analyzing human feelings expressed in the opinions and feedback of web users helps understand general moods and support market strategies for product advertising and market predictions. This paper proposes a framework for emotion-based classification from social streams, such as Twitter, according to Plutchik's wheel of emotions. An entropy-based weighted version of the fuzzy c-means (FCM) clustering algorithm, called EwFCM, to classify the data collected from streams has been proposed, improved by a fuzzy entropy method for the FCM center cluster initialization. Experimental results show that the proposed framework provides high accuracy in the classification of tweets according to Plutchik's primary emotions; moreover, the framework also allows the detection of secondary emotions, which, as defined by Plutchik, are the combination of the primary emotions. Finally, a comparative analysis with a similar fuzzy clustering-based approach for emotion classification shows that EwFCM converges more quickly with better performance in terms of accuracy, precision, and runtime. Finally, a straightforward mapping between the computed clusters and the emotion-based classes allows the assessment of the classification quality, reporting coherent and consistent results.

Fuzzy C-Means Clustering Algorithm-Based Magnetic Resonance Imaging Image Segmentation for Analyzing the Effect of Edaravone on the Vascular Endothelial Function in Patients with Acute Cerebral Infarction.

This paper aimed to discuss the denoising ability of magnetic resonance imaging (MRI) images based on fuzzy C-means clustering (FCM) algorithm and the influence of Butylphthalide combined with Edaravone treatment on nerve function and vascular endothelial function in patients with acute cerebral infarction (ACI). Based on FCM algorithm, Markov Random Field (MRF) model algorithm was introduced to obtain a novel algorithm (NFCM), which was compared with FCM and MRF algorithm in terms of misclassification rate (MCR) and difference of Kappa index (KI). 90 patients with ACI diagnosed in hospital from December 2018 to December 2019 were selected as subjects, who were divided into combined treatment group (conventional treatment + Edaravone + Butylphthalide) and Edaravone group (conventional treatment + Edaravone) randomly, each consisting of 45 cases. The National Institutes of Health Stroke Scale (NIHSS) score and endothelial function index level such as plasma nitric oxide (NO), human endothelin-1 (ET-1), and vascular endothelial cell growth factor (VEGF) were compared before and after treatment between the two groups. The results showed that the MCR of NFCM was evidently inferior to FCM and MRF, and the KI was notably higher relative to the other two algorithms. After treatment, the NIHSS score of the combined treatment group was (9.09 ± 1.86) points and that of Edaravone group was (14.97 ± 3.44) points, with evident difference between the two groups (P < 0.05). After treatment, the NO of the combined treatment was (54.63 ± 4.85), and that of Edaravone group was (41.54 ± 5.27), which was considerably different (P < 0.01), and the VEGF and ET-1 of combined treatment group were greatly inferior to Edaravone group (P < 0.01). It was revealed that the novel algorithm based on FCM can obtain more favorable quality and segmentation accuracy of MRI images. Moreover, Butylphthalide combined with Edaravone treatment can effectively improve nerve function, vascular endothelial function, and short-term prognosis in ACI, which was safe and worthy of clinical adoption.

Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method

The primary means to promote grid-connected photovoltaic power generation is through accurately forecasting the power output from photovoltaic power stations. This paper proposes a method for day-ahead photovoltaic power forecasting (PPF) and uncertainty analysis using fuzzy c-means (FCM), whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and non-parametric kernel density estimation (NPKDE). The FCM clustering algorithm was used to cluster historical data on numerical weather prediction and photovoltaic power stations, whereby daily data sharing similar meteorological information were clustered into one class. The rapid convergence speed and high convergence accuracy of the WOA were used to optimize the penalty factor and kernel function width of the LSSVM model; this was done to improve the calculation speed and forecasting accuracy of the LSSVM model. The WOA-LSSVM forecasting model was trained using the clustered numerical weather prediction and historical data of a photovoltaic power station. This was subsequently utilized to forecast day-ahead photovoltaic power. The NPKDE method was used to accurately calculate the probability density distribution of forecasting error and the confidence interval of the day-ahead PPF. The root mean square error (RMSE) values of the forecasting power of the WOA-LSSVM, PSO-LSSVM, LSSVM, LSTM and PSO-BP models are 2.55%, 3.00%, 5.60%, 6.03% and 3.18%, respectively, and the calculation results show that the forecasting accuracy of the WOA-LSSVM was higher relative to other models including PSO-LSSVM, LSSVM, LSTM and PSO-BP. Moreover, the NPKDE method was able to accurately describe the probability density distribution of the forecasting error.

Contrast enhancement and speckle suppression in OCT images based on a selective weighted variational enhancement model and an SP-FOOPDE algorithm.

Simultaneous contrast enhancement and speckle suppression in optical coherence tomography (OCT) are of great significance to medical diagnosis. In this paper, we propose a selective weighted variational enhancement (SWVE) model to enhance the structural parts of OCT images, and then present a shape-preserving fourth-order-oriented partial differential equations (SP-FOOPDE) algorithm to suppress speckle noise. To be specific, in the SWVE model, we first introduce the fast and robust fuzzy c-means clustering (FRFCM) algorithm to generate masks based on the gray-level histograms of the reconstructed OCT images and utilize the masks to distinguish the structural parts from the background. Then the retinex-based weighted variational model, combined with gamma correction, is adopted to enhance the structural parts by multiplying the estimated reflectance with the adjusted illumination. In the despeckling process, we present an SP-FOOPDE algorithm with the fidelity term modified by the shearlet transform to strike a splendid balance between noise suppression and structural preservation. Experimental results show that the proposed method performs well in contrast enhancement and speckle suppression, with better quality metrics of the MSE, PSNR, CNR, ENL, EKI, and ν and better noise immunity than the related method. Moreover, the application to the segmentation preprocessing exhibits that the retinal structure of the OCT images processed by the proposed method can be completely segmented.

An approach for document retrieval using cluster-based inverted indexing

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

General fuzzy C-means clustering algorithm using Minkowski metric

As one of the most commonly used clustering methods, fuzzy clustering technique such as the Fuzzy C-means (FCM) has undergone a rapid development. In this paper, a general FCM clustering algorithm based on contraction mapping (cGFCM) is proposed for more general cases of using Minkowski metric (Lp-norm distance) as the similarity measure, and the analytical method for calculating the parameters of the proposed algorithm is given. The core of the proposed cGFCM algorithm lies on constructing a contraction mapping to update the prototypes when an arbitrary Minkowski metric is used to measure the closeness of data points. Subsequently, mainly guided by the Banach contraction mapping principle, the algorithm and implementation approaches are discussed in detail, and the correctness and feasibility of the proposed method are proved. Moreover, the convergence of the proposed algorithm is also discussed. Experimental studies carried out on both synthetic data sets and real-world data sets show that the proposed cGFCM algorithm extends FCM to more general cases without extra time and space costs. Compared with another generalized FCM clustering strategy and other five state-of-the-art clustering methods, the proposed algorithm can not only reach better performance in both clustering accuracy and stability, but reduce the running time several-fold.

Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications

Separating an image into regions according to some criterion is called image segmentation. This paper presents an algorithm that combines the fuzzy k-means (FKM) and fuzzy c-means (FCM) clustering strategies. The proposed algorithm combines the FKM and the FCM algorithms mathematical features, which is referred to as (CFKCM). The FKM and FCM clustering algorithms are adopted to compare the performance and hence evaluate the proposed clustering algorithm. Tests are conducted, and performance parameters are calculated for validation. The comparison and assessment analysis are based on metrics related to the image clustering process, such as the Segmentation Accuracy (SA), Clustering Fitness (CF), and cluster Validity function (V_pc and V_pe). A dataset of MR images is used by this research for the application, test, and evaluation of the image clustering. The results for clustering backbone MRI images show that CFKCM algorithm is featured by being more effective, and comparatively independent of the noise, where it can process the

Fusion for Video Enhancement using Fuzzy-C means Clustering Algorithm

Fusion for Video Enhancement using Fuzzy-C means Clustering Algorithm

Collaborative feature-weighted multi-view fuzzy c-means clustering

Fuzzy c-means (FCM) clustering had been extended for handling multi-view data with collaborative idea. However, these collaborative multi-view FCM treats multi-view data under equal importance of feature components. In general, different features should take different weights for clustering real multi-view data. In this paper, we propose a novel multi-view FCM (MVFCM) clustering algorithm with view and feature weights based on collaborative learning, called collaborative feature-weighted MVFCM (Co-FW-MVFCM). The Co-FW-MVFCM contains a two-step schema that includes a local step and a collaborative step. The local step is a single-view partition process to produce local partition clustering in each view, and the collaborative step is sharing information of their memberships between different views. These two steps are then continuing by an aggregation way to get a global result after collaboration. Furthermore, the embedded feature-weighted procedure in Co-FW-MVFCM can give feature reduction to exclude redundant/irrelevant feature components during clustering processes. Experiments with several data sets demonstrate that the proposed Co-FW-MVFCM algorithm can completely identify irrelevant feature components in each view and that, additionally, it can improve the performance of the algorithm. Comparisons of Co-FW-MVFCM with some existing MVFCM algorithms are made and also demonstrated the effectiveness and usefulness of the proposed Co-FW-MVFCM clustering algorithm.

Multi‐objective optimization of drying conditions for the Olea europaea L. leaves with NSGA‐II

Phenolic compounds from olive leaves, which attract great attention with their high phenolic content and high antioxidant activity, need to be dried to increase the extraction efficiency before extraction. For this purpose, it is aimed to obtain optimal drying conditions of hot air inlet temperature (Tg), orifice inlet air flow rate (ν), drying time (t), and rehydration ratio (RR) for moisture removal of olive leaves. For this purpose, final moisture content (MC) and moisture loss (ML) were modeled and optimized by using soft computing methods. Pareto sets were obtained from predicted quadratic linear models using the least-squares (LS) approach. The nondominated sorting genetic algorithm-II (NSGA-II) was used for optimization. The compromise solution was obtained using the fuzzy c-means clustering algorithm (FCM) algorithm, one of the multicriteria decision-making methods. When the olive leaf is dried under optimum conditions determined by FCM (Tg = 63.84°C, v = 1.700 m/s, t = 997 s, RR = 2.21%), the experimental values for total phenolic content (TPC) and oleuropein amount were (20.07 ± 1.01) mg GAE/g dw and (7.30 ± 0.71) mg/g dw, respectively. It was found that drying olive leaves under low airflow rate and low hot air input temperature resulted in less alteration of TPC and oleuropein amount. Practical applications After drying olive leaves under optimum conditions, high value-added compounds and phytochemicals can be obtained. A chemometric study based on soft computing approaches can be used for a drying process. Optimization of predicted fuzzy responses can be achieved in a multi-objective perspective through the nondominated sorting genetic algorithm-II (NSGA-II). Soft computing-based modeling and optimization tools can be applied to determine the optimum conditions of chemical, food, and pharmaceutical industry processes.

An Efficient Segmentation of Biomedical Images using Optimized Level Set Method via Enhanced Possibilistic FCM Clustering

The important aspect to deal with medical images is image-segmentation. Image-segmentation is the way to remove the vicinity of attentiveness using different image segments. This segmentation helps to analyze the representation of the image more efficiently. The segmentation of the image in medical image analysis is taken as a challenging task because it is concerned with numerous clinical applications. To have a segmentation of colonoscopy images, cardiac vascular, knee image and MRI (Magnetic Resonance Imaging) brain images', a new approach in this paper is put forward by combining the particle swarm optimization (PSO) and enhanced possibilistic Fuzzy C-Means algorithm. The proposed algorithm proceeds in two stages: In the first stage, optimum pixel values are calculating automatically from the input medical images by using the PSO algorithm. These optimum pixels are acted as a random cluster centres for enhanced Possibilistic Fuzzy C-Means (EP-FCM) clustering algorithm. This process improves the clustering efficiency because of optimum random clusters are choosing instead of normal cluster centres. Segments obtained in the pre-processing are incoherent and highly noisy in clustered results. Therefore, Post-processing is necessary to effectively reduced the noise and boundary leakages (outliers’). In the Second Stage, it is necessary to refine the segmentation results in the pre-processing stage by using the level-set method for robustness. In this paper, tested proposed segmentation algorithm on medical images such as MRI brain and CT Colonoscopy images. The performance of the algorithm is proven to be outstanding with the best accuracy and has dealt with noise effect, boundary leakages effectively.