A modified possibilisticfuzzyc-meansclustering algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities and noise. By introducing a novel...
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A modified possibilisticfuzzyc-meansclustering algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities and noise. By introducing a novel adaptive method to compute the weights of local spatial in the objective function, the new adaptive fuzzyclustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus allowing the suppression of noise and helping to resolve classification ambiguity. To estimate the intensity inhomogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm and takes the local and global intensity information into account. The segmentation target therefore is driven by two forces to smooth the derived optimal bias field and improve the accuracy of the segmentation task. The proposed method has been successfully applied to 3T, 7T, synthetic and real MR images with desirable results. comparisons with other approaches demonstrate the superior performance of the proposed algorithm. Moreover, the proposed algorithm is robust to initialization, thereby allowing fully automatic applications. (c) 2010 Elsevier Ltd. All rights reserved.
Due to advances in information technology, data collection is becoming much easier. clustering is an important technique for exploring data structures used in many fields, such as customer segmentation, image recognit...
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Due to advances in information technology, data collection is becoming much easier. clustering is an important technique for exploring data structures used in many fields, such as customer segmentation, image recognition, social science, and so on. However, in real-world applications, there are a lot of noises or outliers which will seriously influence the clustering performance in the dataset. Besides, the clustering results are susceptible to the initial centroids and algorithm parameters. To overcome the influence of outliers on clustering results, this study combines the advantages of probability c-means and fuzzyc-ordered means to propose a fuzzypossibilisticc-ordered means (FPcOM) algorithm. In order to solve the problem of parameters and initial centroids determination, this study employs a sine cosine algorithm (ScA) combined with FPcOM to improve the clustering results. The proposed algorithm is named ScA-FPcOM algorithm. Ten benchmark datasets collected from the UcI machine repository were used to validate the proposed algorithm in terms of adjusted rand index and the Silhouette coefficient. According to the experimental results, the ScA-FPcOM algorithmcan obtain better results than other algorithms.
Finding the target consumers for a business depends heavily on its market segmentation approach. Applying clustering analysis to consumer segmentation is one of the most common methods. However, most clustering algori...
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Finding the target consumers for a business depends heavily on its market segmentation approach. Applying clustering analysis to consumer segmentation is one of the most common methods. However, most clustering algorithms can easily fall into the local optimum solution. Besides, it can be challenging to handle noise and outliers and determine the optimal parameters. The initial cluster centers can also affect the clustering result. Thus, this study proposes a clustering algorithm that first employs density peak clustering to obtain the initial cluster centers. Then, the proposed method integrates geneticalgorithm (GA) with possibilisticfuzzyc-means (PFcM) algorithm, where GA is used to optimize the cluster centers and the parameters of the PFcM algorithm to overcome the problems above. Using eleven benchmark datasets, the computational results demonstrate that the proposed algorithmcan provide better and more robust results in terms of accuracy, adjusted rand index (ARI), and normalized mutual information (NMI) compared to previous clustering algorithms. Additionally, the proposed algorithm is used to segment customers of a retail company in a dataset containing recency, frequency, and monetary (RFM) variables. The clustering result for customer segmentation is also very promising.
Breast cancer, is a type of cancer that originates in the breast tissue. For the earlier detection of breast cancer, mammography is considered as the best modality by finding malignant (cancerous) lesions, masses and ...
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ISBN:
(纸本)9781538615072
Breast cancer, is a type of cancer that originates in the breast tissue. For the earlier detection of breast cancer, mammography is considered as the best modality by finding malignant (cancerous) lesions, masses and microcalcifications (Mc's) in the breast tissue. Mc's are the calcium deposits in the breast tissues with no regular patterns, shape and size. They are often located across non- homogeneous backgrounds;their intensity could be similar to that of noise. So, it is difficult to detect in the naked eyes even by the experienced radiologists. To assist them, a fully automated computer Aided Diagnosis (cAD) system has been proposed. In the proposed work, mammogram images are enhanced using Discrete Wavelet Transform (DWT) for denoising and localization. The spatial location range High-High (HH) from DWT is considered and represented as data vector. It is, then, segmented using possibilisticfuzzyc-meansclustering (PFcM) algorithm. PFcM clustering algorithm segments the image into normal tissues and Mc's suspected regions by finding atypicality values of each pixel of data vector. Finally, features of the segments are extracted using window-based feature extraction method and given as input to the Multi-Layer Perceptron (MLP) classifier to classify the tissue as normal tissue or malignant tissue. As per the experiments and results of the proposed system, the accuracy is calculated as 96.15%.
To realize the rational planning of integrated energy stations, this paper introduce a planning method for integrated energy station which based on multiple energy system's basic planning. This planning method con...
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ISBN:
(纸本)9781538614273
To realize the rational planning of integrated energy stations, this paper introduce a planning method for integrated energy station which based on multiple energy system's basic planning. This planning method contains the approach which based on c-meansalgorithm for forecasting users' daily energy load, the approach which based on Voronoi diagram for locating new multiple power stations and the planning model which aims to reduce the annual cost (includs the costs of equipments, operation, maintenance and fuels) of the power stations for power stations' planning. The steps of locating and station planning are all included in this method. This paper solves the model with actual data. By comparing the planning results, the feasibility of the method and the economy of the integrated energy system are verified.
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