Francisco J. Veredas

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.

Automatic design synthesis with artificial intelligence techniques

Abstract Design synthesis represents a highly complex task in the field of industrial design. The main difficulty in automating it is the definition of the design and performance spaces, in a way that a computer can generate optimum solutions. Following a different line from the machine learning, and knowledge-based methods that have been proposed, our approach considers design synthesis as an optimization problem. From this outlook, neural networks and genetic algorithms can be used to implement the fitness function and the search method needed to achieve optimum design. The proposed method has been tested in designing a telephone handset. Although the objective of this application is based on esthetic and ergonomic cues (subjective information), the algorithm successfully converges to good solutions.

Factors determining the precision of the correlated firing generated by a monosynaptic connection in the cat visual pathway

Across the visual pathway, strong monosynaptic connections generate a precise correlated firing between presynaptic and postsynaptic neurons. The precision of this correlated firing is not the same within thalamus and visual cortex. While retinogeniculate connections generate a very narrow peak in the correlogram (peak width < 1 ms), the peaks generated by geniculocortical and corticocortical connections have usually a time course of several milliseconds. Several factors could explain these differences in timing precision such as the amplitude of the monosynaptic EPSP (excitatory postsynaptic potential), its time course or the contribution of polysynaptic inputs. While it is difficult to isolate the contribution of each factor in physiological experiments, a first approximation can be done in modelling studies. Here, we simulated two monosynaptically connected neurons to measure changes in their correlated firing as we independently modified different parameters of the connection. Our results suggest that the precision of the correlated firing generated by strong monosynaptic connections is mostly determined by the EPSP time course of the connection and much less by other factors. In addition, we show that a polysynaptic pathway is unlikely to emulate the correlated firing generated by a monosynaptic connection unless it generates EPSPs with very small latency jitter.

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.

Sulphur Atoms from Methionines Interacting with Aromatic Residues Are Less Prone to Oxidation.

Methionine residues exhibit different degrees of susceptibility to oxidation. Although solvent accessibility is a relevant factor, oxidation at particular sites cannot be unequivocally explained by accessibility alone. To explore other possible structural determinants, we assembled different sets of oxidation-sensitive and oxidation-resistant methionines contained in human proteins. Comparisons of the proteins containing oxidized methionines with all proteins in the human proteome led to the conclusion that the former exhibit a significantly higher mean value of methionine content than the latter. Within a given protein, an examination of the sequence surrounding the non-oxidized methionine revealed a preference for neighbouring tyrosine and tryptophan residues, but not for phenylalanine residues. However, because the interaction between sulphur atoms and aromatic residues has been reported to be important for the stabilization of protein structure, we carried out an analysis of the spatial interatomic distances between methionines and aromatic residues, including phenylalanine. The results of these analyses uncovered a new determinant for methionine oxidation: the S-aromatic motif, which decreases the reactivity of the involved sulphur towards oxidants.

Methionine residues around phosphorylation sites are preferentially oxidized in vivo under stress conditions

Protein phosphorylation is one of the most prevalent and well-understood protein modifications. Oxidation of protein-bound methionine, which has been traditionally perceived as an inevitable damage derived from oxidative stress, is now emerging as another modification capable of regulating protein activity during stress conditions. However, the mechanism coupling oxidative signals to changes in protein function remains unknown. An appealing hypothesis is that methionine oxidation might serve as a rheostat to control phosphorylation. To investigate this potential crosstalk between phosphorylation and methionine oxidation, we have addressed the co-occurrence of these two types of modifications within the human proteome. Here, we show that nearly all (98%) proteins containing oxidized methionine were also phosphoproteins. Furthermore, phosphorylation sites were much closer to oxidized methionines when compared to non-oxidized methionines. This proximity between modification sites cannot be accounted for by their co-localization within unstructured clusters because it was faithfully reproduced in a smaller sample of structured proteins. We also provide evidence that the oxidation of methionine located within phosphorylation motifs is a highly selective process among stress-related proteins, which supports the hypothesis of crosstalk between methionine oxidation and phosphorylation as part of the cellular defence against oxidative stress.

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.

A machine learning approach for predicting methionine oxidation sites

BackgroundThe oxidation of protein-bound methionine to form methionine sulfoxide, has traditionally been regarded as an oxidative damage. However, recent evidences support the view of this reversible reaction as a regulatory post-translational modification. The perception that methionine sulfoxidation may provide a mechanism to the redox regulation of a wide range of cellular processes, has stimulated some proteomic studies. However, these experimental approaches are expensive and time-consuming. Therefore, computational methods designed to predict methionine oxidation sites are an attractive alternative. As a first approach to this matter, we have developed models based on random forests, support vector machines and neural networks, aimed at accurate prediction of sites of methionine oxidation.ResultsStarting from published proteomic data regarding oxidized methionines, we created a hand-curated dataset formed by 113 unique polypeptides of known structure, containing 975 methionyl residues, 122 of which were oxidation-prone (positive dataset) and 853 were oxidation-resistant (negative dataset). We use a machine learning approach to generate predictive models from these datasets. Among the multiple features used in the classification task, some of them contributed substantially to the performance of the predictive models. Thus, (i) the solvent accessible area of the methionine residue, (ii) the number of residues between the analyzed methionine and the next methionine found towards the N-terminus and (iii) the spatial distance between the atom of sulfur from the analyzed methionine and the closest aromatic residue, were among the most relevant features. Compared to the other classifiers we also evaluated, random forests provided the best performance, with accuracy, sensitivity and specificity of 0.7468±0.0567, 0.6817±0.0982 and 0.7557±0.0721, respectively (mean ± standard deviation).ConclusionsWe present the first predictive models aimed to computationally detect methionine sites that may become oxidized in vivo in response to oxidative signals. These models provide insights into the structural context in which a methionine residue become either oxidation-resistant or oxidation-prone. Furthermore, these models should be useful in prioritizing methinonyl residues for further studies to determine their potential as regulatory post-translational modification sites.

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.