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.
Highlights
The ultrasound imaging modality has emerged as a viable technique for non-invasive assessment of altered soft tissue characteristics for diagnostic purposes
A real-time machine learning-based approach is presented for accurate classification of burn groups using B-mode ultrasound images
Texture analysis using a grey-level co-occurrence matrix (GLCM) that is drawn from the B-mode ultrasound images is performed to extract the features for the classification of burn groups
Summary
The ultrasound imaging modality has emerged as a viable technique for non-invasive assessment of altered soft tissue characteristics for diagnostic purposes. Our previous studies have shown that the ultrasound elastography fails to identify burn severity with acceptable accuracy when their elastic properties are not sufficiently altered to adequately contrast with the surrounding tissues[6] To address these problems, we propose an ultrasound imaging-based machine learning approach to objectively identify altered tissue characteristics with specific application to classification of thermally treated ex vivo porcine skin tissue. To overcome the limitations of existing techniques, we propose an ultrasound imaging-based burn classification (USBC) method in which the B-mode ultrasound images are directly used to classify burn depth. This is done by first converting the pixel intensity (grey-level) in the B-mode images into a grey-level co-occurrence matrix (GLCM) and generating statistical measures, or features, of the image texture from this matrix. To assess the performance of the SVM classifiers independently, we use leave-one-out cross-validation (LOOCV)
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