Laura Morente

Binary Tissue Classification on Wound Images With Neural Networks and Bayesian Classifiers

A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, or friction. Diagnosis, treatment, and care of pressure ulcers are costly for health services. Accurate wound evaluation is a critical task for optimizing the efficacy of treatment and care. Clinicians usually evaluate each pressure ulcer by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. In this paper, a hybrid approach based on neural networks and Bayesian classifiers is used in the design of a computational system for automatic tissue identification in wound images. A mean shift procedure and a region-growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multilayer perceptrons is trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes which are determined by clinical experts. This training procedure is driven by a k-fold cross-validation method. Finally, a Bayesian committee machine is formed by training a Bayesian classifier to combine the classifications of the k neural networks. Specific heuristics based on the wound topology are designed to significantly improve the results of the classification. We obtain high efficiency rates from a binary cascade approach for tissue identification. Results are compared with other similar machine-learning approaches, including multiclass Bayesian committee machine classifiers and support vector machines. The different techniques analyzed in this paper show high global classification accuracy rates. Our binary cascade approach gives high global performance rates (average sensitivity =78.7% , specificity =94.7% , and accuracy =91.5% ) and shows the highest average sensitivity score ( =86.3%) when detecting necrotic tissue in the wound.

Wound image evaluation with machine learning

A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear or friction. Diagnosis, care and treatment of pressure ulcers can result in extremely expensive costs for health systems. A reliable diagnosis supported by precise wound evaluation is crucial in order to succeed on the treatment decision and, in some cases, to save the patient?s life. However, current clinical evaluation procedures, focused mainly on visual inspection, do not seem to be accurate enough to accomplish this important task. This paper presents a computer-vision approach based on image processing algorithms and supervised learning techniques to help detect and classify wound tissue types that play an important role in wound diagnosis. The system proposed involves the use of the k-means clustering algorithm for image segmentation and compares three different machine learning approaches-neural networks, support vector machines and random forest decision trees-to classify effectively each segmented region as the appropriate tissue type. Feature selection based on a wrapper approach with recursive feature elimination is shown to be effective in keeping the efficacy of the classifiers up and significantly reducing the number of necessary predictors. Results obtained show high performance rates from classifiers based on fitted neural networks, random forest models and support vector machines (overall accuracy on a testing set 95% CI], respectively: 81.87% 80.03%, 83.61%]; 87.37% 85.76%, 88.86%]; 88.08% 86.51%, 89.53%]), with significant differences found between the three machine learning approaches. This study seeks to provide, using standard classification algorithms, a consistent and robust methodological framework as a basis for the development of reliable computational systems to support ulcer diagnosis.

A hybrid learning approach to tissue recognition in wound images

Purpose – Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, and friction. Diagnosis, treatment and care of pressure ulcers involve high costs for sanitary systems. Accurate wound evaluation is a critical task to optimize the efficacy of treatments and health‐care. Clinicians evaluate the pressure ulcers by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. The purpose of this paper is to use a hybrid learning approach based on neural and Bayesian networks to design a computational system to automatic tissue identification in wound images.Design/methodology/approach – A mean shift procedure and a region‐growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multi‐layer perceptrons is trained with inputs...

Efficient detection of wound-bed and peripheral skin with statistical colour models

Abstract A pressure ulcer is a clinical pathology of localised damage to the skin and underlying tissue caused by pressure, shear or friction. Reliable diagnosis supported by precise wound evaluation is crucial in order to success on treatment decisions. This paper presents a computer-vision approach to wound-area detection based on statistical colour models. Starting with a training set consisting of 113 real wound images, colour histogram models are created for four different tissue types. Back-projections of colour pixels on those histogram models are used, from a Bayesian perspective, to get an estimate of the posterior probability of a pixel to belong to any of those tissue classes. Performance measures obtained from contingency tables based on a gold standard of segmented images supplied by experts have been used for model selection. The resulting fitted model has been validated on a training set consisting of 322 wound images manually segmented and labelled by expert clinicians. The final fitted segmentation model shows robustness and gives high mean performance rates [(AUC: .9426 (SD .0563); accuracy: .8777 (SD .0799); F-score: 0.7389 (SD .1550); Cohen’s kappa: .6585 (SD .1787)] when segmenting significant wound areas that include healing tissues.

Tissue Recognition for Pressure Ulcer Evaluation

Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear o friction. Diagnosis, treatment and care of pressure ulcers involve high costs for sanitary systems. Accurate wound evaluation is a critical task for optimizing the effectiveness of treatments and care. Clinicians usually evaluate each pressure ulcer by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. In this article, an approach based on neural networks and committee machines is used in the designing of a computational system for automatic tissue identification on wound images. A mean shift procedure and a region-growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multi-layer perceptrons are trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes which are determined by clinical experts. This training procedure is driven by a k-fold cross-validation method. Our outcomes show high performance scores of a two-stage cascade approach for tissue identification.

A web-based e-learning application for wound diagnosis and treatment

Pressure ulcers (PrU) are considered as one of the most challenging problems that Nursing professionals have to deal with in their daily practice. Nowadays, the education on PrUs is mainly based on traditional lecturing, seminars and face-to-face instruction, sometimes with the support of photographs of wounds being used as teaching material. This traditional educational methodology suffers from some important limitations, which could affect the efficacy of the learning process. This current study has been designed to introduce information and communication technologies (ICT) in the education on PrU for undergraduate students, with the main objective of evaluating the advantages an disadvantages of using ICT, by comparing the learning results obtained from using an e-learning tool with those from a traditional teaching methodology. In order to meet this major objective, a web-based learning system named ePULab has been designed and developed as an adaptive e-learning tool for the autonomous acquisition of knowledge on PrU evaluation. This innovative system has been validated by means of a randomized controlled trial that compares its learning efficacy with that from a control group receiving a traditional face-to-face instruction. Students using ePULab gave significantly better (p<0.01) learning acquisition scores (from pre-test mean 8.27 (SD 1.39) to post-test mean 15.83 (SD 2.52)) than those following traditional lecture-style classes (from pre-test mean 8.23 (SD 1.23) to post-test mean 11.6 (SD 2.52)). In this article, the ePULab software is described in detail and the results from that experimental educational validation study are also presented and analyzed.

Tissue Recognition Approach to Pressure Ulcer Area Estimation with Neural Networks

Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue with high prevalence rates in aged people. Diagnosis and treatment of pressure ulcers involve high costs for sanitary systems. Accurate wound-state evaluation is a critical task for optimizing the effectiveness of treatments. Reliable trace of wound-state evolution can be done by precisely registering the wound area. Clinicians estimate the wound area with often subjective and imprecise manual methods. This article presents a computer-vision approach based on machine hybrid-learning techniques to precise automatic estimation of wound dimensions on pressure ulcer real images taken under non-controlled illumination conditions. The system combines neural networks and Bayesian classifiers to effectively recognize and separate skin and healing regions from wound-tissue regions to be measured. This tissue-recognition approach to wound area estimation gives high performance rates and operates better than a widespread clinical method when approximating real wound areas of variable size.

A Hybrid Approach for Tissue Recognition on Wound Images

Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear o friction. Diagnosis, treatment and care of pressure ulcers involve high costs for sanitary systems. Accurate wound evaluation is a critical task for optimizing the effectiveness of treatments and care. Clinicians usually evaluate each pressure ulcer by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. In this article, a hybrid approach based on neural networks and Bayesian classifiers is used in the designing of a computational system for automatic tissue identification on wound images. A mean shift procedure and a region-growing strategy are implemented for effective region segmentation. Color and texture features are extracted from the segmented regions. A set of k multi-layer perceptrons are trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes which are determined by clinical experts. This training procedure is driven by a k-fold cross-validation method. Finally, a Bayesian committee machine is formed by training a Bayesian classifier to combine the classifications of the k neural networks. Our outcomes show high performance scores of a two-stage cascade approach for tissue identification.

Computer-aided diagnosis in wound images with neural networks

Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear or friction. Diagnosis, care and treatment of pressure ulcers can result in extremely expensive costs for health systems. A reliable diagnosis supported by precise wound evaluation is crucial in order to success on the treatment decision and, in some cases, to save the patients life. However, current evaluation procedures, focused mainly on visual inspection, do not seem to be accurate enough to accomplish this important task. This paper presents a computer-vision approach based on image processing algorithms and supervised learning techniques to help detecting and classifying wound tissue types which play an important role in wound diagnosis. The system proposed involves the use of the k-means clustering algorithm for image segmentation and a standard multilayer perceptron neural network to classify effectively each segmented region as the appropriate tissue type. Results obtained show a high performance rate which enables to support ulcer diagnosis by a reliable computational system.