Texture discrimination based on an optimal utilization of texture features

He, Dong-Chen; Wang, Li; Guibert, J.

doi:10.1016/0031-3203(88)90020-9

Cited by 47 publications

(9 citation statements)

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“…The element S(i,j, v) of a co-occurrence matrix is the estimated probability of going from grey level i to grey level j given the displacement vector v =(dx, dy). This kind of second-order grey level co-occurrence matrix has been shown to be a valid measure of the spatial distribution of grey levels within the image, and has been widely used in practice (Conners et al 1984, He et al 1988, Marceau et al 1989. However there are two 0143-1161/90 $3.00 © 1990 Taylor & Francis LId major obstacles to this approach in practice.…”

Section: Recognition Of Lithological Units In Airborne Sarmentioning

confidence: 97%

Recognition of lithological units in airborne SAR images using new texture features

Wang

1990

International Journal of Remote Sensing

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Synthetic Aperture Radar (SAR) images are used increasingly in applications of remotely-sensed data for land-use investigation and in the recognition of geological features. This is because SAR images are rich in textural information and textural characteristics of an image are found to be useful in identification of lithological units. For this reason, the automatic processing and interpretation of SAR images are often focused on texture analysis, including the extraction of textural information, textural filtering (enhancement of textural perception, and texture classification. This Letter evaluates a set of new texture features which are extracted from the texture spectrum. Some of the traditional texture features, extracted from the co-occurrence matrix, have been used for comparison with the new measures in the discrimination of different lithological units of an airborne SAR image. The results show the promising discriminating performance of the proposed features.

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Section: Recognition Of Lithological Units In Airborne Sarmentioning

confidence: 97%

Recognition of lithological units in airborne SAR images using new texture features

Wang

1990

International Journal of Remote Sensing

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“…To achieve unique way and rotational invariant property, the proposed TU on PSCBW considered the minimum value. In the previous approaches, the TU ranges from 0 to 3561 [4], 0 to 2020 [5,6,7,8], 0 to 255 [10,11,12,13,14,15] and 0 to 79 [9]. To overcome this, the proposed model of TU of PSCB image reduced the overall TU's from 0 to 15.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Pattern based Dimensionality Reduction Model for Age Classification

Vijayakumar¹,

Kiran²,

Murty³

2013

IJCA

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The two most popular statistical methods used to measure the textural information of images are the Grey Level Cooccurrence Matrix (GLCM) and Texture Units (TU) approaches. The novelty of the present paper is, it combines TU and GLCM features by deriving a new model called "Pattern based Second order Compressed Binary (PSCB) image" to classify human age in to four groups. The proposed PSCB model reduces the given 5 x 5 grey level image into a 2 x 2 binary image, while preserving the significant features of the texture. The proposed method intelligently compressed a 5x5 window into a 2x2 window and derived TU on them. Thus the derived TU also represents a TU of a 5x5 window. The TU of the proposed PSCB model ranges from 0 to 15, thus it overcomes the previous disadvantages in evaluating TU's.

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“…Así mismo se calcularon las variables texturales de segundo orden a partir de la matrices de co-ocurrencia del nivel de gris (GLCM), que se construyen a partir del análisis de parejas de píxeles para una distancia y orientaciones dadas entre ellos (Haralick et al, 1973;He et al, 1988;Walker et al, 1995;Maths Works Inc., 2005 a,b;Hall-Beyer, 2007 Cada una a tres distancias (1, 5 y 10 píxeles) y cuatro orientaciones (0º, 90º, 180º y 270º), y sus correspondientes valores medios. Dado que el patrón textural de un corte transversal del tendón rotuliano no parece ser dependiente de la orientación y como una primera aproximación, los cálculos se realizaron sobre los valores medios obtenidos para cada una de las cuatro orientaciones.…”

Section: Variables Texturalesunclassified

El análisis textural mediante las matrices de co-ocurrencia (GLCM) sobre imagen ecográfica del tendón rotuliano es de utilidad para la detección cambios histológicos tras un entrenamiento con plataforma de vibración. (Textural Analysis by Means of a Grey Level Co-Occurrence Matrix Method on Patellar Tendon Ultrasonography is Useful for the Detection of Histological Changes after Whole-Body Vibration Training)

Ríos-Díaz¹,

Martínez-Payá²,

Aledo³

2009

CCD

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C C D 91 ResumenIntroducción: Las matrices de co-ocurrencia del nivel de gris (GLCM) son útiles para el análisis textural de imágenes ya la discriminación de patrones pero hasta ahora no se han aplicado sobre imágenes ecográficas del tendón. Objetivo: Análisis textural ecográfico del tendón rotuliano. Método: Estudio longitudinal analítico con 16 sujetos (8 mujeres y 8 hombres) jóvenes, sanos y sedentarios entrenados con una plataforma de vibración vertical (Fitvibe Medical) 2 días x 14 semanas. Se tomaron cortes ecográficos transversales del tendón rotuliano antes y después del entrenamiento con un ecógrafo Sonosite-180 (Lineal 5-10 MHz). Mediante el algoritmo GLCM de Image J v1.38 se calcularon las variables texturales Uniformidad (ASM), Contraste, Correlación, Homogeneidad (IDM) y la Entropía para cuatro orientaciones (0º, 90º, 180º y 270º) y tres distancias (d=1, 5 y 10 px). Se aplicó la prueba de Wilcoxon (i.c.95%) para muestras relacionadas (SPSS 15.0). Resultados: la Entropía (d=5) fue la más sensible a los cambios texturales; quizá la variable ASM, pueda resultar también de interés junto con el Contraste. Conclusiones: Ante la falta de referencias con el uso de la GLCM en el análisis textural de ecografía de tendón son necesarios más aná-lisis que estudien cómo afectan los distintos parámetros a las variables texturales, cómo se relacionan entre sí y cuáles pueden ser los mejores ajustes del algoritmo para detectar cambios en el patrón textural.Palabras clave: Matrices de co-ocurrencia de nivel de gris, textura, tendón ecografía, vibración de cuerpo completo. AbstractIntroduction: Co-occurrence grey level matrix (GLCM) is a textural analysis method that have been useful to discriminate patterns, but no used on tendon ultrasound image. Objective: Textural analysis of patellar tendon ultrasonograph. Method: Longitudinal analytic study with 16 subjects (8 women and 8 men) young, healthy and sedentary people with training by means wholebody vibration platform (Fitvibe Medical) for 2 days x 14 weeks. Cross-sectional of patellar tendon ultrasonographics were taken with a Sonosite-180 ultrasonograph (L 5-10 MHz). By means GLCM algorithm of Image J v1.38 it were calculated five textural parameters: Uniformity (ASM), Contrast, Correlation, Homogeneity (IDM) and Entropy in four orientations (0º, 90º, 180º and 270º) and three distances between pixels (d=1, 5 and 10 pixels). Wilcoxon test (C.I. 95%) for related samples was applied (SPSS 15.0). Results: Entropy (d=5) was the most sensible to detect textural changes; perhaps ASM and Contrast can be also useful. It seems that distances between pairs of pixels that the algorithm uses affects the results. Conclusions: The use of GLCM in the textural analysis of tendon ultrasonography is innovating and it appears like a useful tool as much to evaluate the histological evolution of tendon tissue, like coming up and detecting future pathologies precociously. But more analyses will be necessary to study how different parameters affect texture and how they are related to each...

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Texture discrimination based on an optimal utilization of texture features

Cited by 47 publications

References 10 publications

Recognition of lithological units in airborne SAR images using new texture features

Recognition of lithological units in airborne SAR images using new texture features

Pattern based Dimensionality Reduction Model for Age Classification

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