This paper proposes an automatic brain tumor segmentation using Mean shift clustering and content based active contour segmentation. In diagnosis of the disease medical imaging has more advantages. Many people suffer from brain tumor, it is a serious and dangerous disease. A brain tumor occurs when abnormal cells form in the brain. A proper diagnosis of brain tumor is provided by the medical imaging. The detection of tumor from brain is an important and difficult task in the medical field. One essential part in detecting the tumor is image segmentation. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. In view of high amount information in MRI pictures, tumor segmentation and classification are hard. The image segmentation is performed on different dataset of MRI cerebrum tumor pictures. The segmentation gives an automatic brain tumor recognition method to build the exactness, yields with decline in the analysis time. The image segmentation technique includes image acquisition, image preprocessing, denoising, and finally the feature extraction. The input image is pre-processed using wiener filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it undergoes segmentation process, where Mean Shift Clustering and Content based active segmentation techniques are used. Finally, the features are extracted from the segmented image using gray level co-occurrence matrix (GLCM). The image segmentation is implemented using MATLAB software. Finally, the tumor is segmented and energy, contrast, correlation, homogeneity is extracted, and comparison results are analyzed.
Data is being generated at an increasing rate in a variety of fields as science and technology advance. The generated data are being saved for future decision-making. Data mining is the process of extracting patterns and useful information from massive amounts of data. The distance measure, which is used to calculate how different two objects are from one another, is one such instrument. We have conducted a comprehensive survey of how the distance measures behave when employed with different algorithms. Furthermore, the effectiveness and performance of some novel similarity measures proposed by other authors are investigated.
With enormous development of digital technology, data is being generated at rapid rate with various application domains. Data has to be extracted or filtered to find useful information. A basic concept for these tasks and applications are the distance measures to effectively determine how similar two objects are. In this paper, a novel similarity measure for clustering text documents is proposed using the cardinality of the terms in the documents. The bench mark algorithm k-medoids is used for clustering task. The results obtained from the proposed distance measure are compared with other standard distance measures like Manhattan, Euclidean distance measure. Dunn Index is used to analyze the cluster validation of the results obtained from the distance measure.
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