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Your search keyword '"Gray Level Co-occurrence Matrix"' showing total 404 results
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404 results on '"Gray Level Co-occurrence Matrix"'

401. Image Quality Analysis Using GLCM

Author

Gadkari, Dhanashree

Subjects
Image quality, Gray level co-occurrence matrix, GLCM, Other Psychology
Abstract

Gray level co-occurrence matrix has proven to be a powerful basis for use in texture classification. Various textural parameters calculated from the gray level co-occurrence matrix help understand the details about the overall image content. The aim of this research is to investigate the use of the gray level co-occurrence matrix technique as an absolute image quality metric. The underlying hypothesis is that image quality can be determined by a comparative process in which a sequence of images is compared to each other to determine the point of diminishing returns. An attempt is made to study whether the curve of image textural features versus image memory sizes can be used to decide the optimal image size. The approach used digitized images that were stored at several levels of compression. GLCM proves to be a good discriminator in studying different images however no such claim can be made for image quality. Hence the search for the best image quality metric continues.

Published
2004

402. Nonlinear optical microscopy signal processing strategies in cancer.

Author

Adur J, Carvalho HF, Cesar CL, and Casco VH

Abstract

This work reviews the most relevant present-day processing methods used to improve the accuracy of multimodal nonlinear images in the detection of epithelial cancer and the supporting stroma. Special emphasis has been placed on methods of non linear optical (NLO) microscopy image processing such as: second harmonic to autofluorescence ageing index of dermis (SAAID), tumor-associated collagen signatures (TACS), fast Fourier transform (FFT) analysis, and gray level co-occurrence matrix (GLCM)-based methods. These strategies are presented as a set of potential valuable diagnostic tools for early cancer detection. It may be proposed that the combination of NLO microscopy and informatics based image analysis approaches described in this review (all carried out on free software) may represent a powerful tool to investigate collagen organization and remodeling of extracellular matrix in carcinogenesis processes.

Published
2014
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403. Automatic Classification of Woven Fabric Structure by Using Learning Vector Quantization

Author

Junfeng Jing, Jing Wang, Pengfei Li, and Yang Li

Subjects
Learning vector quantization, Engineering, Speedup, business.industry, Structure (category theory), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Wavelet transform, Pattern recognition, General Medicine, Machine learning, computer.software_genre, Identification (information), Matrix (mathematics), Woven fabric, Feature (machine learning), Artificial intelligence, business, computer, Engineering(all), Woven Fabric Structure, Two-dimensional Wavelet Transform, Gray Level Co-occurrence Matrix, Learning vector quantization neural network
Abstract

Current procedure for classification of woven fabric structure in the textile industry is performed by human,which wastes manpower and time.This paper proposes an automatic woven fabric image extraction and classification method.To speed up the system running time,2-D wavelet transform for fabric images is used firstly.Extracting the feature parameters of gray level co-occurrence matrix is a typical method for fabric texture image.Finally, identification and classification of the woven fabric structures exactly by using the learning vector quantization neural network.The experimental results show that focusing on learning vector quantization improves the classification performance as well as increases the computational efficiency.

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404. Ultrasound image uniformity assessment by means of sparse matrices: Algorithm implementation and first results

Author

Schinaia, L., Scorza, A., Francesco Orsini, Sciuto, S. A., Alexandru Salceanu and Cristian Fosalau, Schinaia, Lorenzo, Scorza, A., Orsini, Francesco, and Sciuto, S. A.

Subjects
Uniformity measurement, Gray Level Co-occurrence Matrix, Speckle, Modeling and Simulation, Ultrasound, Electrical and Electronic Engineering, Haralick feature
Abstract

The current study is focused on an image segmentation algorithm for Uniformity Quality assessment in Diagnostic Ultrasounds.In particular a mathematical definition of the uniformity in ultrasound images is introducedand a split and merge algorithm performed on sparse matricesto measure uniformity is described. The algorithm is based on the Gray-Level Co-occurrence Matrices and the relativedescriptors,i.e. the Haralick features Entropy, Energy, Maximal CorrelationCoefficientand InformationMeasures of Correlation.Results on2 differentdatasetsof test images with different non-uniformities have been carried on. Several outcomesshow a goodsensitivity and agreement with the mean judgment by 7 human observers, i.e. differences are below 40% in most of the cases.On the basis of previousstudies, the latest developments and results are proposed and commented.

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