Evaluation of Texture for Classification of Abdominal Aortic Aneurysm After Endovascular Repair

García, Guillermo; Maiora, Josu; Tapia, A.; Blas, Mariano De

doi:10.1007/s10278-011-9417-7

Cited by 29 publications

(25 citation statements)

References 28 publications

(26 reference statements)

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“…In terms of low-contrast resolution, most previous studies have reported unanimous results that an increase in the percentage of ASIR resulted in increased low-contrast resolution, which is consistent with our results. 2 , 5 , 7 , 10 The CNR was calculated from the HU difference between the cavity and the background divided by the noise value. As noise decreases in proportion to the blending percentage of ASIR without changing the HU of both the cavity and the background, the CNR could only increase.…”

Section: Discussionmentioning

confidence: 99%

“…This result was similar to that of a previous study which compared the noise spectrum curve of FBP and ASIR images in a water phantom and showed that applying 40% ASIR was considered optimal when the radiation dose is reduced to 50%. 5 …”

Section: Discussionmentioning

confidence: 99%

“…There are several mathematical methods and computer software programs dedicated to texture analysis. Among them, we adopted the grey-level co-occurrence matrices (GLCM) method, 5 , 6 and an open plug-in of GLCM texture analysis for ImageJ ( http://rsb.info.nih.gov/ij/plugins/ ) was used. Texture analysis was performed with CT images of a low-contrast resolution module, and five consecutive axial images were loaded in ImageJ.…”

Section: Methodsmentioning

confidence: 99%

“… 2 , 3 There are several studies showing that ASIR could reduce radiation doses without degrading diagnostic performance in various organs while providing the optimal blending percentage of ASIR. 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 However, most of these studies involve qualitative image analysis or limited quantitative analysis, such as comparison of the standard deviation (SD) of CT numbers from a sufficient number of pixels. Considering that the images with higher ASIR blending percentages seem to lose their sharpness with artificial appearance, 5 an objective image analysis using more parameters than in previous studies might be needed to determine an optimal blending percentage of ASIR.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Analysis of the Effect of Iterative Reconstruction Using a Phantom: Determining the Appropriate Blending Percentage

et al. 2015

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PurposeTo investigate the optimal blending percentage of adaptive statistical iterative reconstruction (ASIR) in a reduced radiation dose while preserving a degree of image quality and texture that is similar to that of standard-dose computed tomography (CT).Materials and MethodsThe CT performance phantom was scanned with standard and dose reduction protocols including reduced mAs or kVp. Image quality parameters including noise, spatial, and low-contrast resolution, as well as image texture, were quantitatively evaluated after applying various blending percentages of ASIR. The optimal blending percentage of ASIR that preserved image quality and texture compared to standard dose CT was investigated in each radiation dose reduction protocol.ResultsAs the percentage of ASIR increased, noise and spatial-resolution decreased, whereas low-contrast resolution increased. In the texture analysis, an increasing percentage of ASIR resulted in an increase of angular second moment, inverse difference moment, and correlation and in a decrease of contrast and entropy. The 20% and 40% dose reduction protocols with 20% and 40% ASIR blending, respectively, resulted in an optimal quality of images with preservation of the image texture.ConclusionBlending the 40% ASIR to the 40% reduced tube-current product can maximize radiation dose reduction and preserve adequate image quality and texture.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantitative Analysis of the Effect of Iterative Reconstruction Using a Phantom: Determining the Appropriate Blending Percentage

et al. 2015

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“…The GLCM is able to identify subtle local variation of pixel intensities, which is necessary to capture altered texture characteristics for classification of burn tissues using ultrasound images. Both the GLCM and GLRLM are shown to have comparable accuracy in classification of optical images [31][32][33] . In this work, GLCM is used for burn classification.…”

mentioning

confidence: 97%

Real-time Burn Classification using Ultrasound Imaging

Lee

Rahul

et al. 2020

Sci Rep

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This article presents a real-time approach for classification of burn depth based on B-mode ultrasound imaging. A grey-level co-occurrence matrix (GLCM) computed from the ultrasound images of the tissue is employed to construct the textural feature set and the classification is performed using nonlinear support vector machine and kernel Fisher discriminant analysis. A leave-one-out cross-validation is used for the independent assessment of the classifiers. The model is tested for pair-wise binary classification of four burn conditions in ex vivo porcine skin tissue: (i) 200 °F for 10 s, (ii) 200 °F for 30 s, (iii) 450 °F for 10 s, and (iv) 450 °F for 30 s. The average classification accuracy for pairwise separation is 99% with just over 30 samples in each burn group and the average multiclass classification accuracy is 93%. The results highlight that the ultrasound imaging-based burn classification approach in conjunction with the GLCM texture features provide an accurate assessment of altered tissue characteristics with relatively moderate sample sizes, which is often the case with experimental and clinical datasets. The proposed method is shown to have the potential to assist with the real-time clinical assessment of burn degrees, particularly for discriminating between superficial and deep second degree burns, which is challenging in clinical practice.

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