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12 results for “Similarity-Based Segmentation”
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Research ArticleOpen accessPhrase in the titleA Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation TechniqueSaleheh Heidari, Muhamad Taufik Abdullah, Lili Nurliana AbdullahIn automated pulmonary nodules extraction and lung disease diagnosis by image processing techniques, image segmentation is utilized as a primary and the most essential step of lung tumor analysis. But due to extensive similarity between pulmonary vessels, bronchus and arteries in lung region and the low contrast of the Computed-Tomography (CT) image the accuracy of lung tumor diagnosis is highly … …Journal of Computer Science, Vol. 11, Issue 1 · 17 Sep 2014 · 7,477 accessesView PDFCite
Literature ReviewOpen accessIn the abstractEfficient Morphometric Techniques in Alzheimer’s Disease Detection: Survey and ToolsVinutha N., P. Deepa Shenoy, K.R. VenugopalThe development of advance techniques in the multiple fields such as image processing, data mining and machine learning are required for the early detection of Alzheimer’s Disease (AD) and to prevent the progression of the disease to the later stages. The longitudinal and cross sectional images of elderly subjects were obtained from the standard datasets like ADNI, OASIS, MIRIAD and … …Neuroscience International, Vol. 7, Issue 2 · 4 Oct 2017 · 8,450 accessesView PDFCite
EditorialOpen accessIn the abstractData Mining in Time Series: Current Study and Future TrendTan Yan, Liudmila Ulanova, Ye Ouyang, Fengyuan XuTime series represent sequences of data points where usually their order is defined by the time when they were recorded. Thus, virtually any sequential recordings can be stored as time series: Stock prices, weather conditions, physical system parameters change, product quality monitoring, etc. This leads to ubiquity of time series in all scientific and practical fields (Esling and Agon, 2012); … …Journal of Computer Science, Vol. 10, Issue 12 · 26 Jan 2015 · 8,756 accessesView PDFCite
Research ArticleOpen accessIn the abstractNOVEL IMAGE-DEPENDENT QUALITY ASSESSMENT MEASURESZahir M. Hussain, Asaad Noori HashimThe image is a 2D signal whose pixels are highly correlated in a 2D manner. Hence, using pixel by pixel error what we called previously Mean-Square Error, (MSE) is not an efficient way to compare two similar images (e.g., an original image and a compressed version of it). Due to this correlation, image comparison needs a correlative quality measure. It … …Journal of Computer Science, Vol. 10, Issue 8 · 27 Feb 2014 · 7,434 accessesView PDFCite
Research ArticleOpen accessIn the abstractMEDICAL IMAGE SEGMENTATION FOR ANATOMICAL KNOWLEDGE EXTRACTIONMs Maya Eapen, Reeba KorahComputed Tomography-Angiography (CTA) images of the abdomen, followed by precise segmentation and subsequent computation of shape based features of liver play an important role in hepatic surgery, patient/donor diagnosis during liver transplantation and at various treatment stages. Nevertheless, the issues like intensity similarity and Partial Volume Effect (PVE) between the neighboring organs; left the task of liver segmentation critical. The … …Journal of Computer Science, Vol. 10, Issue 7 · 22 Feb 2014 · 6,460 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Review on Recent Research and Implementation Methodologies on Medical Image SegmentationS. K. Somasundaram, P. AlliProblem statement: Image segmentation plays a very important role in digital imaging applications for classifying the anatomical structures and other Regions of Interest. Segmentation refers to the process of identifying and isolating the surface and regions of the digital image which corresponds to the structural units. Approach: In the past few decades, many methods have been proposed to segment the … …Journal of Computer Science, Vol. 8, Issue 1 · 4 Nov 2011 · 7,117 accessesView PDFCite
Research ArticleOpen accessIn the abstractDermoscopic Image Segmentation using Machine Learning AlgorithmL. Padma Suresh, K.L. Shunmuganathan, S.H. Krishna VeniProblem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in … …American Journal of Applied Sciences, Vol. 8, Issue 11 · 7 Oct 2011 · 7,342 accessesView PDFCite
Research ArticleOpen accessIn the abstractObject Recognition Based on Image Segmentation and ClusteringS. Thilagamani, N. ShanthiProblem statement: This study deals with object recognition based on image segmentation and clustering. Acquiring prior information of an image is done via two separate processes. Approach: The first process deals with detecting object parts of an image and integration of detected parts into several clusters. All these cluster centers form the visual words. The second process deals with over … …Journal of Computer Science, Vol. 7, Issue 11 · 6 Sep 2011 · 7,074 accessesView PDFCite
Research ArticleOpen accessIn the abstractImplementation of Computer Aided Diagnosis System Based on Parallel Approach of Ant Based Medical Image SegmentationJayabalan Jaya, Keppanagowder ThanushkodiProblem statement: The aim of this study is to develop Computer Aided Diagnosis (CAD) system for the detection of brain tumor by using parallel implementation of ACO system for medical image segmentation applications due to the rapid execution for obtaining and extracting the Region of Interest (ROI) from the images for diagnostic purposes in medical field. Approach: For ROI segmentation, … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 8,130 accessesView PDFCite
Research ArticleOpen accessIn the abstractGraph Based Segmentation in Content Based Image RetrievalP. S. Suhasini, K. S.R. Krishna, I. V.M. KrishnaProblem statement: Traditional image retrieval systems are content based image retrieval systems which rely on low-level features for indexing and retrieval of images. CBIR systems fail to meet user expectations because of the gap between the low level features used by such systems and the high level perception of images by humans. To meet the requirement as a preprocessing step … …Journal of Computer Science, Vol. 4, Issue 8 · 31 Aug 2008 · 6,581 accessesView PDFCite