Fast algorithms for texture analysis using co-occurrence matrices

Argenti, Fabrizio; Alparone, Luciano; Benelli, G.

doi:10.1049/ip-f-2.1990.0064

Cited by 53 publications

(43 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A gray-level co-occurrence matrix ͑GLCM͒ with pixel offsets ranging from 1 to 10 was used to calculate additional textural feature groups ͑correla-tion, contrast, homogeneity, and energy͒. 13 Each GLCM feature group contained 10 distinct features, corresponding to each pixel offset. To detect nuclear features, an extended regional maximum transform was applied to the image.…”

Section: Image Classification Algorithmmentioning

confidence: 99%

Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett’s esophagus

et al. 2010

View full text Add to dashboard Cite

Abstract. Early detection of neoplasia in patients with Barrett's esophagus is essential to improve outcomes. The aim of this ex vivo study was to evaluate the ability of high-resolution microendoscopic imaging and quantitative image analysis to identify neoplastic lesions in patients with Barrett's esophagus. Nine patients with pathologically confirmed Barrett's esophagus underwent endoscopic examination with biopsies or endoscopic mucosal resection. Resected fresh tissue was imaged with fiber bundle microendoscopy; images were analyzed by visual interpretation or by quantitative image analysis to predict whether the imaged sites were non-neoplastic or neoplastic. The best performing pair of quantitative features were chosen based on their ability to correctly classify the data into the two groups. Predictions were compared to the gold standard of histopathology. Subjective analysis of the images by expert clinicians achieved average sensitivity and specificity of 87% and 61%, respectively. The best performing quantitative classification algorithm relied on two image textural features and achieved a sensitivity and specificity of 87% and 85%, respectively. This ex vivo pilot trial demonstrates that quantitative analysis of images obtained with a simple microendoscope system can distinguish neoplasia in Barrett's esophagus with good sensitivity and specificity when compared to histopathology and to subjective image interpretation.

show abstract

Section: Image Classification Algorithmmentioning

confidence: 99%

Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett’s esophagus

et al. 2010

View full text Add to dashboard Cite

show abstract

“…Statistical measures including contrast, correlation, energy, and homogeneity were computed from the GLCMs. More detail is provided in Argenti et al 24 Twenty-four features were created based on these statistical measures from GLCMs where d varied from 1 to 6. The features were averaged at angles θ = 0, 45, 90, and 135 deg to account for the fact that these multispectral images do not have a specific spatial orientation.…”

Section: Feature Extractionmentioning

confidence: 99%

Comparison of multispectral wide-field optical imaging modalities to maximize image contrast for objective discrimination of oral neoplasia

Roblyer

Kurachi²,

Stepanek

et al. 2010

J. Biomed. Opt.

View full text Add to dashboard Cite

Abstract. Multispectral widefield optical imaging has the potential to improve early detection of oral cancer. The appropriate selection of illumination and collection conditions is required to maximize diagnostic ability. The goals of this study were to (i) evaluate image contrast between oral cancer/precancer and non-neoplastic mucosa for a variety of imaging modalities and illumination/collection conditions, and (ii) use classification algorithms to evaluate and compare the diagnostic utility of these modalities to discriminate cancers and precancers from normal tissue. Narrowband reflectance, autofluorescence, and polarized reflectance images were obtained from 61 patients and 11 normal volunteers. Image contrast was compared to identify modalities and conditions yielding greatest contrast. Image features were extracted and used to train and evaluate classification algorithms to discriminate tissue as non-neoplastic, dysplastic, or cancer; results were compared to histologic diagnosis. Autofluorescence imaging at 405-nm excitation provided the greatest image contrast, and the ratio of red-to-green fluorescence intensity computed from these images provided the best classification of dysplasia/cancer versus non-neoplastic tissue. A sensitivity of 100% and a specificity of 85% were achieved in the validation set. Multispectral widefield images can accurately distinguish neoplastic and non-neoplastic tissue; however, the ability to separate precancerous lesions from cancers with this technique was limited. C 2010 Society of Photo-Optical Instrumentation Engineers.

show abstract

“…[2] Converting the carbon copy into a negative carbon copy. Fragmenting the analysis of carbon copy and removing the components in the fragments.…”

Section: Literary Workmentioning

confidence: 99%

Identification of diseases in plant parts using image processing

Saranya¹,

Kiran²,

Reddy³

2018

IJET

View full text Add to dashboard Cite

Day by day production levels in agriculture has been increasing, similarly diseases in plants also growing vigorously. Detecting damaged parts in leaves succor to develop a software which will help the farmer to get more amount of turn outs, it can blotch the diseases precisely. Therefore, it helps farmers for modern methodology at farming and also helps in good yields. Due to excrescences in trees and plants, it's hard to find them. So, collect carbon copy and process them with several algorithms which gives the best outcome. This assistance helps in geoponics growth. The main purpose of the project is to help for better development in healthy farming outgrowth.

show abstract

Fast algorithms for texture analysis using co-occurrence matrices

Cited by 53 publications

References 10 publications

Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett’s esophagus

Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett’s esophagus

Comparison of multispectral wide-field optical imaging modalities to maximize image contrast for objective discrimination of oral neoplasia

Identification of diseases in plant parts using image processing

Contact Info

Product

Resources

About