In recent days, image processing is an interesting research field and mainly the medical image processing is increasingly challenging field to process various medical image types. It is widely used in diagnosis of disease such as brain tumor, Cancer, Diabetes etc. and brain tumor is one such dangerous disease and currently moreover 600,000 people have this type of disease. Image segmentation is an important technique highly used to extract the suspicious parts from medical images such as MRI, CT scan, and Mammography etc. With this motivation in this work, SOM clustering is proposed for MRI brain image segmentation. Before the segmentation the Histogram Equalization is utilized for feature extraction which will improve the segmentation accuracy. After the segmentation process, the feature extraction using Gray Level Co-occurrence Matrix is utilized which avoids the formation of misclustered regions. The Principle Component Analysis (PCA) method is used for the feature selection to improve the classifier accuracy. An effective classifier Proximal Support Vector Machines (PSVM) is used to automatically detect the tumor from MRI brain image. This method is faster and computationally more efficient than the existing method SVM. While the SOM clustering with Histogram Equalization is a fast procedure for the segmentation of the whole volume and provides a way to model tissue classes, the PSVM-GLCM-PCA approach is a more robust scheme under noisy or bad intensity normalization conditions which produces better results using high resolution images, outperforming the results provided by other algorithms in the state-of-the art, in terms of the average overlap metric.
In this paper, a novel Feature-Reduction Fuzzy C-means (FRFCM) with Feature Linkage Weight (FRFCM-FLW) algorithm is introduced. By the combination of FRFCM and feature linkage weight, we develop a new feature selection model, called a Feature Linkage Weight Based FRFCM using fuzzy clustering. The larger amounts of features are superior to the complication of the problem, and the larger the time that is exhausted in creating the outcome of the classifier or the model. Feature selection has been established as a high-quality method for preferring features that best describes the data under certain criteria or measure. The proposed method presents three stages namely, 1) Data Formation: The process of data collection and data cleaning; 2) FRFCM-FLW. The proposed method can decrease feature elements routinely, and also construct excellent clustering results. The proposed method calculates a novel weight for every feature by combining modified Mahalanobis distance with feature & deltam variance in FRFCM algorithm; 3) Fuzzy C-means (FCM) cluster. The proposed FRFCM-FLW method proves high Accuracy Rate (AR), Rand Index (RI) and Jaccard Index (JI) ratio when compared to other feature reduction algorithms like WFCM, EWKM, WKM, FCM and FRFCM algorithms.
Over the last few years, data are generated in large volume at a faster rate and there has been a remarkable growth in the need for large scale data processing systems. As data grows larger in size, data quality is compromised. Functional dependencies representing semantic constraints in data are important for data quality assessment. Executing functional dependency discovery algorithms on a single computer is hard and laborious with large data sets. MapReduce provides an enabling technology for large scale data processing. The open-source Hadoop implementation of MapReduce has provided researchers a powerful tool for tackling large-data problems in a distributed manner. The objective of this study is to extract functional dependencies between attributes from large datasets using MapReduce programming model. Attribute entropy is used to measure the inter attribute correlations, and exploited to discover functional dependencies hidden in the data.
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