Texture descriptors based on co-occurrence matrices

Gotlieb, C. C.; Kreyszig, Herbert E.

doi:10.1016/s0734-189x(05)80063-5

Cited by 201 publications

(38 citation statements)

References 8 publications

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“…In the experiments reported, normal/abnormal tissue sample discrimination is based upon the analysis of the grey-level co-occurrence matrices [14,15]. This method evaluates a series of matrices that describe the spatial variation of grey-level values within a local area.…”

Section: Methodsmentioning

confidence: 99%

“…The second category, i.e. the statistical process, involves the use of statistical tools and inference: grey-level cooccurrence matrices [14,15], grey-level run length statistics [16,17], grey-level difference [18] and second-order moments [19]. In the third category, corresponding to random process, textured images are considered as realisations, or samples of spatial random fields.…”

Section: Textural Information For the Detection Of Abnormalitiesmentioning

confidence: 99%

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Image recognition and neuronal networks: Intelligent systems for the improvement of imaging information

Karkanis

Magoulas

Theofanous

2000

Minimally Invasive Therapy & Allied Technologies

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Intelligent computerised systems can provide useful assistance to the physician in the rapid identification of tissue abnormalities and accurate diagnosis in real-time. This paper reviews basic issues in medical imaging and neural network-based systems for medical image interpretation. In the framework of intelligent systems, a simple scheme that has been implemented is presented as an example of the use of intelligent systems to discriminate between normal and cancerous regions in colonoscopic images. Preliminary results indicate that this scheme is capable of high accuracy detection of abnormalities within the image. It can also be successfully applied to different types of images, to detect abnormalities that belong to different cancer types.

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Section: Methodsmentioning

confidence: 99%

Section: Textural Information For the Detection Of Abnormalitiesmentioning

confidence: 99%

Image recognition and neuronal networks: Intelligent systems for the improvement of imaging information

Karkanis

Magoulas

Theofanous

2000

Minimally Invasive Therapy & Allied Technologies

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“…Huit indices de cooccurrence normalisés ( Figure 6) sont calculés et créés à partir de l'ensemble de la matrice mais seulement trois indices ont été retenu homogénéité, dissemblance et moyenne qui accentuent les principaux éléments structuraux (Gotlieb & Kreyszig, 1990;Kouamé et al, 1999;Hanifi, 2009). …”

Section: Traitement De Transformationunclassified

Cartographie des linéaments géologiques en domaine aride par extraction semi-automatique à partir d’images satellitaires: Exemple à la région d’El Kseïbat (Sahara algérien)

Hammad¹,

Djidel²,

Maabedi³

2016

Estud. geol.

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“…This computation is done separately on all units. Color [8] and textures features are: mean and standard deviation over each color band, co-occurrence matrices [9], Gabor filters [10], Laws filters [11], Hue moments [12] and fractal dimension [13]. We obtain 25 normalized features integrating geometrical, statistical, frequential and fractal approaches.…”

Section: B Homogeneity Criterionmentioning

confidence: 99%

Raster-Vector Cooperation Algorithm for GIS - Application to Ecological Units Delineation

Grandchamp

2010

2010 Second International Conference on Advanced Geographic Information Systems, Applications, and Services

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During the last decade, Geographic InformationSystems (GIS) considerably evolved: the amount of stored data explodes and the tools used to treat them have been improved. During the same period the arrival of very high spatial resolution satellite images (less than 1 meter) gives an inexhaustible source of information actually largely unexploited. We generally use images (satellite or airborne) in GIS by stacking vector information to visualize thematic maps but without exploiting the images as an information source. We propose in this paper a way to use satellite images as an information source in order to (i) produce new information layers (ii) validate some fusion operation made with the other information layers.We present an iterative algorithm to select and fuse layers (with eventually a relaxation). We use an homogeneity criterion based on local texture analysis to validate the selection. After running this algorithm, new information layers are produced using the images and depending on the previous fusions and selections. We apply this algorithm to search Ecological Units within forest in order to guide the research of observation samples for the underlying ecosystems.

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Texture descriptors based on co-occurrence matrices

Cited by 201 publications

References 8 publications

Image recognition and neuronal networks: Intelligent systems for the improvement of imaging information

Image recognition and neuronal networks: Intelligent systems for the improvement of imaging information

Cartographie des linéaments géologiques en domaine aride par extraction semi-automatique à partir d’images satellitaires: Exemple à la région d’El Kseïbat (Sahara algérien)

Raster-Vector Cooperation Algorithm for GIS - Application to Ecological Units Delineation

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