Tensor Decomposition for Colour Image Segmentation of Burn Wounds

Marco D Cirillo,Robin Mirdell,Tuan D Pham,Folke Sjöberg

doi:10.1038/s41598-019-39782-2

Abstract

Research in burns has been a continuing demand over the past few decades, and important advancements are still needed to facilitate more effective patient stabilization and reduce mortality rate. Burn wound assessment, which is an important task for surgical management, largely depends on the accuracy of burn area and burn depth estimates. Automated quantification of these burn parameters plays an essential role for reducing these estimate errors conventionally carried out by clinicians. The task for automated burn area calculation is known as image segmentation. In this paper, a new segmentation method for burn wound images is proposed. The proposed methods utilizes a method of tensor decomposition of colour images, based on which effective texture features can be extracted for classification. Experimental results showed that the proposed method outperforms other methods not only in terms of segmentation accuracy but also computational speed.

Highlights

The World Health Organization has guidelines for burn treatment that, at least, there must be one bed in a burn unit for each 500,000 inhabitants[3]
This project proposes a new method for the burn-wound image segmentation using a method of tensor decomposition that can extract effective luminance-colour texture features for classification of burn and non-burn areas
The tensor decomposition is independent from the camera resolution, because it works on the CIELab tensor model instead on the number of pixels of the image

Summary

Introduction

The World Health Organization has guidelines for burn treatment that, at least, there must be one bed in a burn unit for each 500,000 inhabitants[3]. Given limited burn treatment facilities over a large geographic environment, especially in middle and low income countries, and the importance of burn area calculation, the demand for developing automated methods for accurate and objective assessment of burn parameters have been increasingly realized in burn research. This project proposes a new method for the burn-wound image segmentation using a method of tensor decomposition that can extract effective luminance-colour texture features for classification of burn and non-burn areas.

Methods

Results

Conclusion