Texture Analysis

Tüceryan, Mihran; Jain, Anil K.

doi:10.1142/9789814343138_0010

Cited by 426 publications

(139 citation statements)

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“…This is due to the following reasons: (1) most of the spectral energy of natural image often centres at low frequency. There is very little energy at high frequencies [1]. (2) Furthermore, it can keep the computational efficiency.…”

Section: Parameter Selectionmentioning

confidence: 99%

“…There are several research focuses in the field of texture analysis, mainly including texture classification, texture segmentation, texture synthesis, shape from texture, etc [1]. Texture segmentation aims at localizing the boundaries between different textures on one textured image plane by classifying pixels based on their texture properties.…”

Section: Introductionmentioning

confidence: 99%

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Invariant texture segmentation via circular Gabor filters

Zhang

Tan

Object Recognition Supported by User Interaction for Service Robots

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Section: Parameter Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Invariant texture segmentation via circular Gabor filters

Zhang

Tan

Object Recognition Supported by User Interaction for Service Robots

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“…All related texture features from the statistical-based texture analysis were generated from each retinal image that derived from gray level co-occurrence matrix (GLCM) and run-length matrix [17][18][19][20][21][22][23][24][25].…”

Section: ) Statistical Based Texture Analysis For Image Processingmentioning

confidence: 99%

“…The grey level run-length matrix (RLM) is defined as the numbers of runs with pixels of gray level i and run length j for a given direction [23]. RLMs was generated for each sample image segment having directions (0˚, 45˚, 90˚ & 135˚), then the following five statistical features were derived: short run emphasis, long run emphasis, gray level non-uniformity, run length non-uniformity and run percentage.…”

Section: ) Statistical Based Texture Analysis For Image Processingmentioning

confidence: 99%

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

Lee¹,

Zee²,

Li³

2013

JBiSE

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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.

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“…The texture of an image can be simply defined as a function of spatial variation in pixel intensities [20]. Numerous methods have been proposed to study texture, and these methods can be classified into four categories: statistical methods, model based methods, geometrical (structural, syntactic) methods, and signal processing methods [20].…”

Section: Introductionmentioning

confidence: 99%

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

et al. 2010

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Breast cancer is a leading cause of death nowadays in women throughout the world. In developed countries, it is the most common type of cancer in women, and it is the second or third most common malignancy in developing countries. The cancer incidence is gradually increasing and remains a significant public health concern. The limitations of mammography as a screening and diagnostic modality, especially in young women with dense breasts, necessitated the development of novel and more effective strategies with high sensitivity and specificity. Thermal imaging (thermography) is a noninvasive imaging procedure used to record the thermal patterns using Infrared (IR) camera. The aim of this study is to evaluate the feasibility of using thermal imaging as a potential tool for detecting breast cancer. In this work, we have used 50 IR breast images (25 normal and 25 cancerous) collected from Singapore General Hospital, Singapore. Texture features were extracted from co-occurrence matrix and run length matrix. Subsequently, these features were fed to the Support Vector Machine (SVM) classifier for automatic classification of normal and malignant breast conditions. Our proposed system gave an accuracy of 88.10%, sensitivity and specificity of 85.71% and 90.48% respectively.

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Texture Analysis

Cited by 426 publications

References 0 publications

Invariant texture segmentation via circular Gabor filters

Invariant texture segmentation via circular Gabor filters

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

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