In this paper, after an overview of the literature concerning the imaging technologies applied to skin wounds assessment, we present an original approach to build 3-D models of skin wounds from color images. The method can deal with uncalibrated images acquired with a handheld digital camera with free zooming. Compared with the cumbersome imaging systems already proposed, this novel solution uses a low-cost and user-friendly image acquisition device suitable for widespread application in health care centers. However, this method entails the development of a robust image processing chain. An original iterative matching scheme is used to generate a dense estimation of the surface geometry from two widely separated views. The best configuration for taking photographs lies between 15 ( degrees ) and 30 ( degrees ) for the vergency angle. The metric reconstruction of the skin wound is fully automated through self-calibration. From the 3-D model of the skin wound, accurate volumetric measurements are achieved. The accuracy of the inferred 3-D surface is validated by registration to a ground truth and repetitive tests on volume. The global precision around 3% is in accordance with the clinical requirement of 5% for assessing the healing process.

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This work is part of the ESCALE project dedicated to the design of a complete 3D and color wound assessment tool using a simple free handled digital camera. The first part was concerned with the computation of a 3D model for wound measurements using uncalibrated vision techniques. This paper presents the second part which deals with color classification of wound tissues, a prior step before to combine shape and color analysis in a single tool for real tissue surface measurements. As direct pixel classification proved to be inefficient for tissue wound labeling, we have adopted an original approach based on unsupervised segmentation prior to classification, to improve the robustness of the labeling step by considering spatial continuity and homogeneity. A ground truth is first provided by merging the images collected and labeled by clinicians. Then, color and texture tissue descriptors are extracted on labeled regions of this learning database to design a SVM region classifier, achieving 88% success overlap score. Finally, we apply unsupervised color region segmentation on test images and classify the regions. Compared to the ground truth, segmentation driven classification and clinician labeling achieve similar performance, around 75% for granulation and 60% for slough.

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We developed a hyperspectral imaging system in order to enhance some biological tissue visualization. The proposed methods provided an acceptable trade-off between the evaluation criteria especially in SWIR spectral band that outperforms the naked eye's capacities.

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This paper is concerned with the 3D modeling of skin wound using uncalibrated vision techniques for the volumetric assessment of the healing process. We have developed an original approach for matching two color images captured with a free-handled digital camera and generate a semi-dense 3D model. We evaluate the precision of the inferred 3D model by registration to a ground truth on artificial wounds. The method is then applied to volumetric measurements. The clinician requirements of a global 5% precision are overshot as 3% is obtained locally. The best configuration for taking photos lies between 1.2 and 1.5 for distance ratios and between 15 degrees and 30 degrees for vergence of the stereo pair. This work is part of the ESCALE project dedicated to the design of a complete 3D and color wound assessment tool using a simple free handled digital camera: a smart solution for massive diffusion in care centers as such very low cost system should be operated directly by nurses.

Foot ulceration is the most common complication of diabetes and represents a major health problem all over the world. If these ulcers are not adequately treated in an early stage, they may lead to lower limb amputation. Considering the low-cost and prevalence of smartphones with a high-resolution camera, Diabetic Foot Ulcer (DFU) healing assessment by image analysis became an attractive option to help clinicians for a more accurate and objective management of the ulcer. In this work, we performed DFU segmentation using Deep Learning methods for semantic segmentation. Our aim was to find an accurate fully convolutional neural network suitable to our small database. Three different fully convolutional networks have been tested to perform the ulcer area segmentation. The U-Net network obtained a Dice Similarity Coefficient of 97.25% and an intersection over union index of 94.86%. These preliminary results demonstrate the power of fully convolutional neural networks in diabetic foot ulcer segmentation using a limited number of training samples.