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The features that control discrimination of an isodipole texture pair.

ABSTRACT: Visual features such as edges and corners are carried by high-order statistics. Previous analysis of discrimination of isodipole textures, which isolate specific high-order statistics, demonstrates visual sensitivity to these statistics but stops short of analyzing the underlying computations. Here we use a new texture centroid paradigm to probe these computations. We focus on two canonical isodipole textures, the even and odd textures: any 2?×?2 block of even (odd) texture contains an even (odd) number of black (and white) checks. Each stimulus comprised a spatially random array of black-and-white texture-disks (background?=?mean gray) that varied in their fourth-order statistics. In the Even (Odd) condition, disks varied along the continuum between random coinflip texture and pure (highly structured) even (odd) target texture. The task was to mouse-click the centroid of the disk array, weighting each disk location by the target structure level of the disk-texture (ranging from 0 for coinflip to 1 for even or odd). For each of block-sizes S=2×2, 2?×?3, 2?×?4 and 3?×?3, a linear model was used to estimate the weight exerted on the subject's responses by the differently patterned blocks of size S. Only the results with 2?×?4 and 3?×?3 blocks were consistent with the data. In the Even condition, homogeneous blocks exerted the most weight; in the odd condition, block-pattern symmetry was important. These findings show that visual mechanisms sensitive to four-point correlations do not compute evenness or oddness per se, but rather are activated selectively by features whose frequency varies across isodipole textures.

SUBMITTER: Groulx K

PROVIDER: S-EPMC6658094 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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The features that control discrimination of an isodipole texture pair.

Groulx Kier K Chubb Charles C Victor Jonathan D JD Conte Mary M MM

Vision research 20190326

Visual features such as edges and corners are carried by high-order statistics. Previous analysis of discrimination of isodipole textures, which isolate specific high-order statistics, demonstrates visual sensitivity to these statistics but stops short of analyzing the underlying computations. Here we use a new texture centroid paradigm to probe these computations. We focus on two canonical isodipole textures, the even and odd textures: any 2 × 2 block of even (odd) texture contains an even (odd ...[more]

PMID: 30885878

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Project description:AimCancer treatments with radiation present a challenging physical toll for patients, which can be justified by the potential reduction in cancerous tissue with treatment. However, there remain patients for whom treatments do not yield desired outcomes. Radiomics involves using biomedical images to determine imaging features which, when used in tandem with retrospective treatment outcomes, can train machine learning (ML) classifiers to create predictive models. In this study we investigated whether pre-treatment imaging features from index lymph node (LN) quantitative ultrasound (QUS) scans parametric maps of head & neck (H&N) cancer patients can provide predictive information about treatment outcomes.Methods72 H&N cancer patients with bulky metastatic LN involvement were recruited for study. Involved bulky neck nodes were scanned with ultrasound prior to the start of treatment for each patient. QUS parametric maps and related radiomics texture-based features were determined and used to train two ML classifiers (support vector machines (SVM) and k-nearest neighbour (k-NN)) for predictive modeling using retrospectively labelled binary treatment outcomes, as determined clinically 3-months after completion of treatment. Additionally, novel higher-order texture-of-texture (TOT) features were incorporated and evaluated in regards to improved predictive model performance.ResultsIt was found that a 7-feature multivariable model of QUS texture features using a support vector machine (SVM) classifier demonstrated 81% sensitivity, 76% specificity, 79% accuracy, 86% precision and an area under the curve (AUC) of 0.82 in separating responding from non-responding patients. All performance metrics improved after implementation of TOT features to 85% sensitivity, 80% specificity, 83% accuracy, 89% precision and AUC of 0.85. Similar trends were found with k-NN classifier.ConclusionBinary H&N cancer treatment outcomes can be predicted with QUS texture features acquired from index LNs. Prediction efficacy improved by implementing TOT features following methodology outlined in this work.

Project description:ObjectiveTo determine the intra-, inter- and test-retest variability of CT-based texture analysis (CTTA) metrics.Materials and methodsIn this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra-scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post-reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test-retest) and robustness (intra-scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter-scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust.ResultsAs expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform-based texture metrics was overall most reliable across the two scanners and scanning conditions. Post-processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used.ConclusionFollowing large-scale validation, identification of reliable CTTA metrics can aid in conducting large-scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.

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The features that control discrimination of an isodipole texture pair.

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The features that control discrimination of an isodipole texture pair.

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