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Search Results: 1 - 10 of 43133 matches for " Texture Analysis "
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Texture feature based automated seeded region growing in abdominal MRI segmentation  [PDF]
Jie Wu, Skip Poehlman, Michael D. Noseworthy, Markad V. Kamath
Journal of Biomedical Science and Engineering (JBiSE) , 2009, DOI: 10.4236/jbise.2009.21001
Abstract: A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- variogram texture features are extracted from the image and a seeded region growing algorithm is run on these feature spaces. With a given Region of Interest (ROI), a seed point is automatically se-lected based on three homogeneity criteria. A threshold is then obtained by taking a lower value just before the one causing ‘explosion’. This algorithm is tested on 12 series of 3D ab-dominal MR images.
Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy  [PDF]
Jack Lee, Benny Zee, Qing Li
Journal of Biomedical Science and Engineering (JBiSE) , 2013, DOI: 10.4236/jbise.2013.63038

Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated at an early stage. Exudates are the primary sign of DR. Currently there is no fully automated method to detect exudates in the literature and it would be useful in large scale screening if fully automatic method is available. In this paper we developed a novel method to detect exudates that based on interactions between texture analysis and segmentation with mathematical morphological technique by using multimodel inference. The texture analysis involves three components: they are statistical texture analysis, high order spectra analysis, and fractal analysis. The performance of the proposed method is assessed by the sensitivity, specificity and accuracy using the public data DIARETDB1. Our results show that the sensitivity, specificity and accuracy are 95.7%, 97.6% and 98.7% (SE = 0.01), respectively. It is shown that the proposed method can be run automatically and also improve the accuracy of exudates detection significantly over most of the previous methods.

Combinations of Feature Descriptors for Texture Image Classification  [PDF]
Alexander Barley, Christopher Town
Journal of Data Analysis and Information Processing (JDAIP) , 2014, DOI: 10.4236/jdaip.2014.23009
Abstract: Texture recognition and classification is a widely applicable task in computer vision. A key stage in performing this task is feature extraction, which identifies sets of features that describe the visual texture of an image. Many descriptors can be used to perform texture classification; among the more common of these are the grey level co-occurrence matrix, Gabor wavelets, steerable pyramids and SIFT. We analyse and compare the effectiveness of these methods on the Brodatz, UIUCTex and KTH-TIPS texture image datasets. The efficacy of the descriptors is evaluated both in isolation and by combining several of them by means of machine learning approaches such as Bayesian networks, support vector machines, and nearest-neighbour approaches. We demonstrate that using a combination of features improves reliability over using a single feature type when multiple datasets are to be classified. We determine optimal combinations for each dataset and achieve high classification rates, demonstrating that relatively simple descriptors can be made to perform close to the very best published results. We also demonstrate the importance of selecting the optimal descriptor set and analysis techniques for a given dataset.
Effect of Wholemeal Durum Wheat Varieties on Bread Quality  [PDF]
Alessandra Danza, Marcella Mastromatteo, Lucia Lecce, Sara Spinelli, Janine Laverse, Vincenzo Lampignano, Francesco Contò, Matteo Alessandro Del Nobile
Food and Nutrition Sciences (FNS) , 2014, DOI: 10.4236/fns.2014.511108

In this work the effect of six varieties of durum wheat semolina on the bread physico-chemical and sensorial properties was addressed. In particular, whole grains of durum wheat (Anco Marzio, Claudio, Core, Iride, Saragolla and Cappelli) were finely milled by using an ancient stone milling system. Texture analysis was carried out on both dough and bread samples to evaluate their firmness. Furthermore, tomographic analysis was performed on the bread samples in order to provide a more detailed view of their texture. The Glucose Equivalent, the chemical and the sensory analyses of the bread were also determined. Results highlighted that the lowest Glucose Equivalent valuewas achieved in bread produced with the Anco Marzio cultivar, which appeared instead the worst in terms of texture. Among the investigated samples, bread from the Cappelli variety showed good structural characteristic, a moderate Glucose Equivalent compared to the reference sample (CTRL) and the highest sensory quality.

Classification of Emphysema Subtypes: Comparative Assessment of Local Binary Patterns and Related Texture Features  [PDF]
Mizuho Nishio, Hisanobu Koyama, Yoshiharu Ohno, Kazuro Sugimura
Advances in Computed Tomography (ACT) , 2015, DOI: 10.4236/act.2015.43007
Abstract: The purpose of this study was to assess usefulness of local binary patterns (LBP) and related texture features, namely completed local binary patterns (CLBP) and local ternary patterns (LTP), for the classification of emphysema subtypes on low-dose CT images. Fifty patients (34 men and 16 women; age, 67.5 ± 10.1 years) who underwent low-dose CT (60 mAs) were included. They were comprised of 17 never smokers, 13 smokers without COPD, and 20 smokers with COPD. By consensus reading of low-dose CT images from these patients, two radiologists selected 3681 nonoverlapping regions of interest (ROIs) and annotated them as one of the following three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. From these ROIs, histogram of CT densities, LBP, CLBP, and LTP were calculated, and the 3 types of texture histograms were concatenated with the CT density histogram. These 3 types of histograms (referred to as combined LBP, combined CLBP, and combined LTP) were used to classify ROI using linear support vector machine. For each type of the combined histogram, the accuracy of classification was determined by patient-based 10-fold cross validation. The best accuracy of combined LBP, combined CLBP, and combined LTP were 81.36%, 82.99%, and 83.29%, respectively. Compared to the classification accuracies obtained with combined LBP, those with combined LTP or combined CLBP were consistently improved. In conclusion, the results of this study suggest that, on low-dose CT, LTP and CLBP were more useful for the classification of emphysema subtypes than LBP.
Quantification of Annual Urban Growth of Dar es Salaam Tanzania from Landsat Time Series Data  [PDF]
Kamara Emanuel Gombe, Ichio Asanuma, Jong-Geol Park
Advances in Remote Sensing (ARS) , 2017, DOI: 10.4236/ars.2017.63013
Abstract: The information on urban land cover distribution and its dynamics is useful for understanding urbanization and its impacts on the hydrological cycle, water management, surface energy balances, urban heat island, and biodiversity. This study utilizes machine learning, texture variables and spectral bands to quantify the urban growth annually. We used multi-temporal Landsat satellite image sets from 2007 to 2016 and Random Forest classification to map urban land-use in Dar es Salaam. We also applied Annual classification approach to detect the spatiotemporal patterns of urban areas. This approach improved classification accuracy and aided in understanding the urban land-use system dynamics operating in our study area. The results pointed out that, the total built-up areas have grown from 318 km2, 388.6 km2 and 634.7 km2 in 2007, 2012 and 2016 respectively. The built up areas growth rate is almost 8%, which makes Dar es Salaam be among the fastest growing cities in Africa. The results indicate that, combining spectral bands, texture variables (NDVI BCI, MNDWI) and annual classification map approach was sufficient to map the urban areas. The approach applied in this research provides a useful guide to the urban growth studies and may also serve as a tool for land management planners.
Comparison of Computerized and Vvisual Texture Analysis of Liver Focal Lesions in MRI
A Gharbali,RA Lerski,SJ Gandy,R Bhat
Iranian Journal of Public Health , 2005,
Abstract: Even though each the focal liver lesions image has, it special pattern but in most of case differentiation between them is not easy task for radiologist. It seems computer aided differentiation can be useful in this step of diagnostic. Two independent radiologist assessed slice of MR liver images and twenty-three patients with focal liver lesions (3 Cyst, 6 Haemangioma and 14 Metastasis) and 10 normal livers were chosen for study. A texture analysis software and mathematical software were utilized to differentiate region of interest (ROI) among and in between ill and healthy liver slice images based on their differences in texture parameters. Linear discrimination analysis (LDA), Principle Component Analysis (PCA), combinations, and fusions of LDA and PCA were used as classification methods. Multiple ROIs were defined on control images to find out their best features data and linear discrimination functions for differentiation with high rate confidence. Sample images examined using the control set examination findings. The results then compared with radiologist reports. All classification methods allowed discrimination among and between healthy and focal lesion regions on the images. Automated texture analysis concurred with radiological diagnosis in all Cyst patients and all but one metastasis report. However, Haemangioma reports were classified as metastasis lesions. All samples of normal livers and normal parts of metastasis liver were correctly differentiated from metastasis. But more than 50% of patients reported as a metastasis diagnosed as normal. Comparison with visual diagnostic reports of MR liver images suggest that automated texture analysis has the potential to improve classification rates in the radiological diagnosis.
Some Numerical Characteristics of Image Texture
O. Samarina,V. Slavsky
European Researcher , 2012,
Abstract: Texture classification is one of the basic images processing tasks. In this paper we present some numerical characteristics to the images analysis and processing. It can be used at the solving of images classification problems, their recognition, problems of remote sounding, biomedical images analysis, geological researches.
Text Extraction in Complex Color Document Images for Enhanced Readability  [PDF]
P. Nagabhushan, S. Nirmala
Intelligent Information Management (IIM) , 2010, DOI: 10.4236/iim.2010.22015
Abstract: Often we encounter documents with text printed on complex color background. Readability of textual contents in such documents is very poor due to complexity of the background and mix up of color(s) of foreground text with colors of background. Automatic segmentation of foreground text in such document images is very much essential for smooth reading of the document contents either by human or by machine. In this paper we propose a novel approach to extract the foreground text in color document images having complex background. The proposed approach is a hybrid approach which combines connected component and texture feature analysis of potential text regions. The proposed approach utilizes Canny edge detector to detect all possible text edge pixels. Connected component analysis is performed on these edge pixels to identify candidate text regions. Because of background complexity it is also possible that a non-text region may be identified as a text region. This problem is overcome by analyzing the texture features of potential text region corresponding to each connected component. An unsupervised local thresholding is devised to perform foreground segmentation in detected text regions. Finally the text regions which are noisy are identified and reprocessed to further enhance the quality of retrieved foreground. The proposed approach can handle document images with varying background of multiple colors and texture; and foreground text in any color, font, size and orientation. Experimental results show that the proposed algorithm detects on an average 97.12% of text regions in the source document. Readability of the extracted foreground text is illustrated through Optical character recognition (OCR) in case the text is in English. The proposed approach is compared with some existing methods of foreground separation in document images. Experimental results show that our approach performs better.
Texture analysis of computed tomography images of acute ischemic stroke patients
Oliveira, M.S.;Fernandes, P.T.;Avelar, W.M.;Santos, S.L.M.;Castellano, G.;Li, L.M.;
Brazilian Journal of Medical and Biological Research , 2009, DOI: 10.1590/S0100-879X2009005000034
Abstract: computed tomography (ct) images are routinely used to assess ischemic brain stroke in the acute phase. they can provide important clues about whether to treat the patient by thrombolysis with tissue plasminogen activator. however, in the acute phase, the lesions may be difficult to detect in the images using standard visual analysis. the objective of the present study was to determine if texture analysis techniques applied to ct images of stroke patients could differentiate between normal tissue and affected areas that usually go unperceived under visual analysis. we performed a pilot study in which texture analysis, based on the gray level co-occurrence matrix, was applied to the ct brain images of 5 patients and of 5 control subjects and the results were compared by discriminant analysis. thirteen regions of interest, regarding areas that may be potentially affected by ischemic stroke, were selected for calculation of texture parameters. all regions of interest for all subjects were classified as lesional or non-lesional tissue by an expert neuroradiologist. visual assessment of the discriminant analysis graphs showed differences in the values of texture parameters between patients and controls, and also between texture parameters for lesional and non-lesional tissue of the patients. this suggests that texture analysis can indeed be a useful tool to help neurologists in the early assessment of ischemic stroke and quantification of the extent of the affected areas.
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