Supervised texture classification by integration of multiple texture methods and evaluation windows

García, Miguel Ángel; Puig, Domènec

doi:10.1016/j.imavis.2006.05.023

Cited by 30 publications

(32 citation statements)

References 29 publications

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“…Following this, a set of 11 textures features (or descriptors) was obtained for each GLCM by applying to these matrices the following quantifier functions: energy, entropy, correlation, contrast, homogeneity, variance, sum-mean, cluster shade, cluster tendency, maximum probability, and inverse variance. These standard functions are commonly used in radiomic analyses, and capture various properties of tissue heterogeneity [34][35][36][37][38][39]. The final region representations, composed of 11 features, are obtained by averaging features individually across the four GLCMs and all 2D slices containing the region.…”

Section: Glcm Based Texture Featuresmentioning

confidence: 99%

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

et al. 2017

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Background: Emerging evidence suggests the presence of neuroanatomical abnormalities in subjects with autism spectrum disorder (ASD). Identifying anatomical correlates could thus prove useful for the automated diagnosis of ASD. Radiomic analyses based on MRI texture features have shown a great potential for characterizing differences occurring from tissue heterogeneity, and for identifying abnormalities related to these differences. However, only a limited number of studies have investigated the link between image texture and ASD. This paper proposes the study of texture features based on grey level co-occurrence matrix (GLCM) as a means for characterizing differences between ASD and development control (DC) subjects. Our study uses 64 T1-weighted MRI scans acquired from two groups of subjects: 28 typical age range subjects 4-15 years old (14 ASD and 14 DC, age-matched), and 36 non-typical age range subjects 10-24 years old (20 ASD and 16 DC). GLCM matrices are computed from manually labeled hippocampus and amygdala regions, and then encoded as texture features by applying 11 standard Haralick quantifier functions. Significance tests are performed to identify texture differences between ASD and DC subjects. An analysis using SVM and random forest classifiers is then carried out to find the most discriminative features, and use these features for classifying ASD from DC subjects. Results:Preliminary results show that all 11 features derived from the hippocampus (typical and non-typical age) and 4 features extracted from the amygdala (non-typical age) have significantly different distributions in ASD subjects compared to DC subjects, with a significance of p < 0.05 following Holm-Bonferroni correction. Features derived from hippocampal regions also demonstrate high discriminative power for differentiating between ASD and DC subjects, with classifier accuracy of 67.85%, sensitivity of 62.50%, specificity of 71.42%, and the area under the ROC curve (AUC) of 76.80% for age-matched subjects with typical age range. Conclusions:Results demonstrate the potential of hippocampal texture features as a biomarker for the diagnosis and characterization of ASD.

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Section: Glcm Based Texture Featuresmentioning

confidence: 99%

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

et al. 2017

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“…This is one of the reasons that there are various feature-based techniques in the literature, each of which tries to model one or several properties of textures depending on the application in hand. The performance of each of these features depends on the texture type, and there is no single feature method that performs well on all different texture types [82,83]. To avoid this problem, objects, for example textures in this case, can be represented in (dis)similarity space.…”

Section: Introductionmentioning

confidence: 99%

Kernelized Supervised Dictionary Learning

Gangeh¹,

Ghodsi

Kamel

2013

IEEE Trans. Signal Process.

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“…A multiple classifier fusion algorithm is proposed for developing an effective video based face recognition method [7]. Garcia and Puig present results showing that pixel-based texture classification can be significantly improved by integrating texture methods from multiple families, each evaluated over multisized windows [8]. This technique consists of an initial training stage that evaluates the behavior of each considered texture method when applied to the given texture patterns of interest over various evaluation windows of different size.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Functional-Based Online Adaptive Decision Fusion Framework With Application to Wildfire Detection in Video

Günay

Töreyi̇n

Kose

et al. 2012

IEEE Trans. on Image Process.

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Abstract-In this paper, an entropy functional based online adaptive decision fusion framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights which are updated online according to an active fusion method based on performing entropic projections onto convex sets describing sub-algorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrives sequentially and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented.Index Terms-Projections onto convex sets, active learning, decision fusion, online learning, entropy maximization, wildfire detection using video.

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Supervised texture classification by integration of multiple texture methods and evaluation windows

Cited by 30 publications

References 29 publications

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

Kernelized Supervised Dictionary Learning

Entropy-Functional-Based Online Adaptive Decision Fusion Framework With Application to Wildfire Detection in Video

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