Breast cancer is one of the most incurable diseases, which leads to the death of women globally every year. For initial detection of a tumor in the breast, the most useful technique called 'Mammography' is use...
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ISBN:
(纸本)9781509020287
Breast cancer is one of the most incurable diseases, which leads to the death of women globally every year. For initial detection of a tumor in the breast, the most useful technique called 'Mammography' is used, which is an X-ray inspection of the breast, which can be used to detect the breast tumor which may lead to breast cancer. Using Mammography, a small lump that may lead to breast cancer can be detected at the initial stage. Sometimes it is not possible to recognize very small tumors because of noisy, blurred, and fuzzy images. Therefore, they need to be enhanced to increase the contrast for better visual perception and reduce the noise from it for better diagnosis. In this work, FcM algorithm is used to detect the suspicious lesions in a mammogram. To achieve the objective of this work, MIAS (Mammographic Image Analysis Society) and INbreast databases are used, which contain 322 and 412 images of the breast (both left and right breast) respectively. In these databases, every image is examined by the expert radiologists. The effectiveness of the algorithm is measured in terms of MSE and PSNR.
In real-world problems, engineering data often suffer from outliers due to deficiencies in measurement techniques, recording errors, or other reasons. In this work, a robust prediction method is proposed based on the ...
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In real-world problems, engineering data often suffer from outliers due to deficiencies in measurement techniques, recording errors, or other reasons. In this work, a robust prediction method is proposed based on the Kriging method and fuzzy c-means algorithm. An outlier detection strategy is designed based on the fuzzy c-means algorithm, in which Kriging method is used to evaluate the relationship between the input and output. The membership of each training sample is calculated based on the prediction error of the Kriging model of each cluster and is used to judge whether the training sample is an outlier. The detected outliers are removed from the training samples, and then the remaining training samples are used to construct the final prediction model. The effect of the parameters of the proposed method on its performance is studied through an one-dimensional and a four-dimensional numerical problem, and ten benchmark functions are used to test its performance thoroughly. The results indicate that the proposed method produces much better performance in terms of outlier detection accuracy and prediction accuracy than the conventional outlier detection method and the Kriging method. Similar results can be found in the experiments on engineering problems. The proposed method is applied to model and analyze the operation data of the cleaning device of a combine harvester. The effect of the operation parameters of the cleaning device on the grain impurity ratio is studied, and the operation parameters of the cleaning device are optimized based on the prediction model of the proposed method. The analysis and optimization results provide a reference for the operation and control of the cleaning device of a combine harvester.
fuzzyclustering is useful to mine complex and multi-dimensional data sets, where the members have partial or fuzzy relations. Among the various developed techniques, fuzzy-c-means (FcM) algorithm is the most popular ...
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fuzzyclustering is useful to mine complex and multi-dimensional data sets, where the members have partial or fuzzy relations. Among the various developed techniques, fuzzy-c-means (FcM) algorithm is the most popular one, where a piece of data has partial membership with each of the pre-defined cluster centers. Moreover, in FcM, the cluster centers are virtual, that is, they are chosen at random and thus might be out of the data set. The cluster centers and membership values of the data points with them are updated through some iterations. On the other hand, entropy-based fuzzyclustering (EFc) algorithm works based on a similarity-threshold value. contrary to FcM, in EFc, the cluster centers are real, that is, they are chosen from the data points. In the present paper, the performances of these algorithms have been compared on four data sets, such as IRIS, WINES, OLITOS and psychosis (collected with the help of forty doctors), in terms of the quality of the clusters (that is, discrepancy factor, compactness, distinctness) obtained and their computational time. Moreover, the best set of clusters has been mapped into 2-D for visualization using a self-organizing map (SOM).
Debris flows in theWudongde dam area, china could pose a huge threat to the running of the power station. Therefore, it is of great significance to carry out a susceptibility analysis for this area. This paper present...
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Debris flows in theWudongde dam area, china could pose a huge threat to the running of the power station. Therefore, it is of great significance to carry out a susceptibility analysis for this area. This paper presents an application of the rock engineering system and fuzzy c-means algorithm (RES_FcM) for debris flow susceptibility assessment. The watershed of the Jinsha River close to the Wudongde dam site in southwest china was taken as the study area, where a total of 22 channelized debris flow gullies were mapped by field investigations. Eight environmental parameters were selected for debris flow susceptibility assessment, namely, lithology, watershed area, slope angle, stream density, length of the main stream, curvature of the main stream, distance from fault and vegetation cover ratio. The interactions among these parameters and their weightings were determined using the RES method. A debris flow susceptibility map was produced by dividing the gullies into three categories of debris flow susceptibility based on the susceptibility index (SI) using the FcM algorithm. The results show that the susceptibility levels for nine of the debris flow gullies are high, nine are moderate and four are low, respectively. The RES based K-meansalgorithm (RES_KM) was used for comparison. The results suggest that the RES_FcM method and the RES_KM method provide very close evaluation results for most of the debris flow gullies, which also agree well with field investigations. The prediction accuracy of the new method is 90.9%, larger than that obtained by the RES_KM method (86.4%). Therefore, the RES_FcM method performs better than the RES_KM method for assessing the susceptibility of debris flows.
The fuzzyc-meansclustering (FcM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionisticfuzzy c-means algorithm based on membership information ...
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The fuzzyc-meansclustering (FcM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionisticfuzzy c-means algorithm based on membership information transferring and similarity measurements (IFcM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FcM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFcM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FcM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFcM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFcM-MS. Experiments performed on real brain tumor images demonstrate that our IFcM-MS has low noise sensitivity and high segmentation accuracy.
cluster analysis is a useful tool used commonly in data analysis. The purpose of cluster analysis is to separate data sets into subsets according to their similarities and dissimilarities. In this paper, the fuzzyc-m...
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cluster analysis is a useful tool used commonly in data analysis. The purpose of cluster analysis is to separate data sets into subsets according to their similarities and dissimilarities. In this paper, the fuzzy c-means algorithm was adapted for directional data. In the literature, several methods have been used for the clustering of directional data. Due to the use of trigonometric functions in these methods, clustering is performed by approximate distances. As opposed to other methods, the FcM4DD uses angular difference as the similarity measure. Therefore, the proposed algorithm is a more consistent clustering algorithm than others. The main benefit of FcM4DD is that the proposed method is effectively a distribution-free approach to clustering for directional data. It can be used for N-dimensional data as well as circular data. In addition to this, the importance of the proposed method is that it would be applicable for decision making process, rule-based expert systems and prediction problems. In this study, some existing clustering algorithms and the FcM4DD algorithm were applied to various artificial and real data, and their results were compared. As a result, these comparisons show the superiority of the FcM4DD algorithm in terms of consistency, accuracy and computational time. fuzzyclustering algorithms for directional data (FcM4DD and FcD) were compared according to membership values and the FcM4DD algorithm obtained more acceptable results than the FcD algorithm. (c) 2016 Elsevier Ltd. All rights reserved.
The geophysical inversion with combining prior information is very important for resource exploration and studies of the Earth's internal structure. Guided fuzzyc-meansclustering inversion (FcM) is normally appl...
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The geophysical inversion with combining prior information is very important for resource exploration and studies of the Earth's internal structure. Guided fuzzyc-meansclustering inversion (FcM) is normally applied for the Tikhonov regularized inversion, but has the shortcoming of uniform model parameter shrinkage, leading to inaccuracy. In this paper, an improved guided fuzzyclustering algorithm is proposed by adding a fuzzy entropy term to the original guided FcM. This method not only enforces the discrete values to a high degree of approximation by guiding the recovered model to cluster tightly around the known petrophysical property values, but also calculates the distributed characteristics of the model parameter set. Based on this method, the shortcoming of uniform shrinkage of the original guided FcM clustering algorithm is improved, and more accurate inversion results are obtained, making the FcM method more efficient and broadly applicable. Furthermore, a new parameter search algorithm is proposed to accelerate the search speed. The results recovered by using this method with three kinds of theoretical gravity anomaly data show more accurate density anomalies compared with the results generated from the original guided FcM clustering inversion and greater efficiency in the parametric search process when using the new parameter search algorithm. The improved FcM clustering algorithmcould enable more extensive and efficient use of gravity inversion.
It is important for the early diagnosis and treatment of lung cancer in the computer-aided Diagnosis/Detection（cAD） system, and accurate segmentation of pulmonary nodules from tomographic images is the basic and act...
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It is important for the early diagnosis and treatment of lung cancer in the computer-aided Diagnosis/Detection（cAD） system, and accurate segmentation of pulmonary nodules from tomographic images is the basic and active research problem for the benign or malign diagnosis. For this reason, this work seeks to develop automatic detection and classification method of lung nodules. First, the algorithm separates lung parenchyma from the anatomical structures based on maximum between-cluster variance, image dilation and erosion. Secondly, a modified robust fuzzyc-meansclustering（r FcM） segmentation algorithm is proposed, this method improves the objective function by adding a punishment factor, for eliminating the influence from noise and non-uniform gray problem. Experimental results have shown that the proposed method can achieve more accurate segmentation and perform better than other traditional algorithms in classification and recognition, Furthermore, the segmentation results on brain images also get a satisfied performance.
The classification recognition performance is a hot study in the field of remote sensing image. In this paper, texture feature, shape feature, radiation intensity of remote sensing image information were used to initi...
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The classification recognition performance is a hot study in the field of remote sensing image. In this paper, texture feature, shape feature, radiation intensity of remote sensing image information were used to initial terrain classification. Then an improved fuzzy c-means algorithm was applied on classification, and it included optimization of determine clustering center, got the number of clustering automatically and removed the noise of image after classification. Meanwhile, as an alternative to expert knowledge, data fusion method was used, which included the fusion of aeromagnetic data, gravity data and elevation data. The empirical results showed that this method can avoid the highly dependent on domain knowledge experts in image recognition and got a better classification effect in remote sensing image.
Breast cancer is one of the most incurable diseases, which leads to the death of women globally every year. For initial detection of a tumor in the breast, the most useful technique called 'Mammography' is use...
详细信息
ISBN:
(纸本)9781509020300
Breast cancer is one of the most incurable diseases, which leads to the death of women globally every year. For initial detection of a tumor in the breast, the most useful technique called 'Mammography' is used, which is an X-ray inspection of the breast, which can be used to detect the breast tumor which may lead to breast cancer. Using Mammography, a small lump that may lead to breast cancer can be detected at the initial stage. Sometimes it is not possible to recognize very small tumors because of noisy, blurred, and fuzzy images. Therefore, they need to be enhanced to increase the contrast for better visual perception and reduce the noise from it for better diagnosis. In this work, FcM algorithm is used to detect the suspicious lesions in a mammogram. To achieve the objective of this work, MIAS (Mammographic Image Analysis Society) and INbreast databases are used, which contain 322 and 412 images of the breast (both left and right breast) respectively. In these databases, every image is examined by the expert radiologists. The effectiveness of the algorithm is measured in terms of MSE and PSNR.
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