Dataset Information

Real-time Burn Classification using Ultrasound Imaging.

ABSTRACT: 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.

SUBMITTER: Lee S

PROVIDER: S-EPMC7118155 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Real-time Burn Classification using Ultrasound Imaging.

Lee Sangrock S Rahul Ye Hanglin H Chittajallu Deepak D Kruger Uwe U Boyko Tatiana T Lukan James K JK Enquobahrie Andinet A Norfleet Jack J De Suvranu S

Scientific reports 20200402 1

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 binar ...[more]

PMID: 32242131

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Project description:BackgroundThere is growing interest in limb contrast-enhanced ultrasound (CEU) perfusion imaging for the evaluation of peripheral artery disease. Because of low resting microvascular blood flow in skeletal muscle, signal enhancement during limb CEU is prohibitively low for real-time imaging. The aim of this study was to test the hypothesis that this obstacle can be overcome by intermediate- rather than low-power CEU when performed with an acoustically resilient microbubble agent.MethodsViscoelastic properties of Definity and Sonazoid were assessed by measuring bulk modulus during incremental increases in ambient pressure to 200 mm Hg. Comparison of in vivo microbubble destruction and signal enhancement at a mechanical index (MI) of 0.1 to 0.4 was performed by sequential reduction in pulsing interval from 10 to 0.05 sec during limb CEU at 7 MHz in mice and 1.8 MHz in dogs. Destruction was also assessed by broadband signal generation during passive cavitation detection. Real-time CEU perfusion imaging with destruction-replenishment was then performed at 1.8 MHz in dogs using an MI of 0.1, 0.2, or 0.3.ResultsSonazoid had a higher bulk modulus than Definity (66 ± 12 vs 29 ± 2 kPa, P = .02) and exhibited less inertial cavitation (destruction) at MIs ≥ 0.2. On in vivo CEU, maximal signal intensity increased incrementally with MI for both agents and was equivalent between agents except at an MI of 0.1 (60% and 85% lower for Sonazoid at 7 and 1.8 MHz, respectively, P < .05). However, on progressive shortening of the pulsing interval, Definity was nearly completely destroyed at MIs ≥ 0.2 at 1.8 and 7 MHz, whereas Sonazoid was destroyed only at 1.8 MHz at MIs ≥ 0.3. As a result, real-time CEU perfusion imaging demonstrated approximately fourfold greater enhancement for Sonazoid at an MI of 0.3 to 0.4.ConclusionsRobust signal enhancement during real-time CEU perfusion imaging of the limb is possible when using intermediate-power imaging coupled with a durable microbubble contrast agent.

Dataset Information

Real-time Burn Classification using Ultrasound Imaging.

Publications

Real-time Burn Classification using Ultrasound Imaging.

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