Automatic Scale Severity Assessment Method in Psoriasis Skin Images Using Local Descriptors.

Psoriasis is a chronic skin condition. Its clinical assessment involves four measures: erythema, scales, induration, and area. In this paper, we introduce a scale severity scoring framework for two-dimensional psoriasis skin images. Specifically, we leverage the bag-of-visual words (BoVWs) model for lesion feature extraction using superpixels as key points. BoVWs model is based on building a vocabulary with specific number of words (i.e., codebook size) by using a clustering algorithm with some local features extracted from a constructed set of key points. This is followed by three-class machine learning classifiers for scale scoring using support vector machine (SVM) and random forest. Besides, we examine eight different local color and texture descriptors, namely color histogram, local binary patterns, edge histogram descriptor, color layout descriptor, scalable color descriptor, color and edge directivity descriptor (CEDD), fuzzy color and texture histogram, and brightness and texture directionality histogram. Further, the selection of codebook and superpixel sizes are studied intensively. A psoriasis image set, consisting of 96 images, is used in this study. The conducted experiments show that color descriptors have the highest performance measures for scale severity scoring. This is followed by the combined color and texture descriptors, whereas texture-based descriptors come last. Moreover, K-means algorithm shows better results in vocabulary building than Gaussian mixed model, in terms of accuracy and computations time. Finally, the proposed method yields a scale severity scoring accuracy of 80.81% using the following setup: a superpixel of size [Formula: see text], a combined color and texture descriptor (i.e., CEDD), a constructed codebook of size 128 using K-means, and SVM for scale scoring.

Haloperidol discontinuation for people with schizophrenia.

BACKGROUND: Schizophrenia is a disabling serious mental illness that can be chronic. Haloperidol, one of the first generation of antipsychotic drugs, is effective in the treatment of schizophrenia but can have adverse side effects. The effects of stopping haloperidol in people with schizophrenia who are stable on their prescription are not well researched in the context of systematic reviews. OBJECTIVES: To review the effects of haloperidol discontinuation in people with schizophrenia who are stable on haloperidol. SEARCH METHODS: On 20 February 2015, 24 May 2017, and 12 January 2019, we searched the Cochrane Schizophrenia Group's Study-Based Register of Trials including trial registers. SELECTION CRITERIA: We included clinical trials randomising adults with schizophrenia or related disorders who were receiving haloperidol, and were stable. We included trials that randomised such participants to either continue their current treatment with haloperidol or discontinue their haloperidol treatment. We included trials that met our selection criteria and reported usable data. DATA COLLECTION AND ANALYSIS: We independently checked all records retrieved from the search and obtained full reports of relevant records for closer inspection. We extracted data from included studies independently. All usable data were dichotomous, and we calculated relative risks (RR) and their 95% confidence intervals (95% CI) using a fixed-effect model. We assessed risk of bias within the included studies and used GRADE to create a 'Summary of findings' table. MAIN RESULTS: We included five randomised controlled trials (RCTs) with 232 participants comparing haloperidol discontinuation with haloperidol continuation. Discontinuation was achieved in all five studies by replacing haloperidol with placebo. The trials' size ranged between 23 and 87 participants. The methods of randomisation, allocation concealment and blinding were poorly reported.Participants allocated to discontinuing haloperidol treatment were more likely to show no improvement in global state compared with those in the haloperidol continuation group (n = 49; 1 RCT; RR 2.06, 95% CI 1.33 to 3.20; very low quality evidence: our confidence in the effect estimate is limited due to relevant methodological shortcomings of included trials). Those who continued haloperidol treatment were less likely to experience a relapse compared to people who discontinued taking haloperidol (n = 165; 4 RCTs; RR 1.80, 95% CI 1.18 to 2.74; very low quality evidence). Satisfaction with treatment (measured as numbers leaving the study early) was similar between groups (n = 43; 1 RCT; RR 0.13, 95% CI 0.01 to 2.28; very low quality evidence).No usable mental state, general functioning, general behaviour or adverse effect data were reported by any of the trials. AUTHORS' CONCLUSIONS: This review provides limited evidence derived from small, short-term studies. The longest study was for one year, making it difficult to generalise the results to a life-long disorder. Very low quality evidence shows that discontinuation of haloperidol is associated with an increased risk of relapse and a reduction in the risk of 'global state improvement'. However, participant satisfaction with haloperidol treatment was not different from participant satisfaction with haloperidol discontinuation as measured by leaving the studies early. Due to the very low quality of these results, firm conclusions cannot be made. In addition, the available studies did not report usable data regarding the adverse effects of haloperidol treatment.Considering that haloperidol is one of the most widely used antipsychotic drugs, it was surprising that only a small number of studies into the benefit and harm of haloperidol discontinuation were available. Moreover, the available studies did not report on outcomes that are important to clinicians and to people with schizophrenia, particularly adverse effects and social outcomes. Better designed trials are warranted.

Biologically Inspired QuadTree Color Detection in Dermoscopy Images of Melanoma.

This paper presents a QuadTree-based melanoma detection system inspired by dermatologists' color perception. Clinical color assessment in dermoscopy images is challenging because of subtle differences in shades, location-dependent color information, poor color contrast, and wide variation among images of the same class. To overcome these challenges, color enhancement and automatic color identification techniques, based on QuadTree segmentation and modeled after expert color assessments, are developed. The approach presented in this paper is shown to provide an accurate model of expert color assessment. Specifically, the proposed model is shown to: 1) identify significantly more colors in melanomas than in benign skin lesions; 2) identify a higher frequency in melanomas of three colors: blue-gray, black, and pink; and 3) delineate locations of melanoma colors by quintiles, specifically predilection for blue-gray and pink in the periphery and a trend for white and black in the lesion center. Performance of the proposed method is evaluated using four classifiers. The kernel support vector machine classifier is found to achieve the best results, with an area under the receiver operating characteristic (ROC) curve of 0.93, compared to average area under the ROC curve of 0.82 achieved by the dermatologists in this study. The results indicate that the biologically inspired method of automatic color detection proposed in this paper has the potential to play an important role in melanoma diagnosis in the clinic.

[Allergic contact dermatitis caused by curcumin-containing chlorhexidine].

Contact dermatitis in connection with skin disinfection with yellow-coloured curcumin containing chlorhexidine solution prior to surgery is very rare. However, this case report presents a 59-year-old patient, who developed pruritic erythematous papules, patches and vesicles over the areas, where curcumin-containing disinfectant was applied. The diagnosis was made by allergic patch test, and the patient was treated with a topical steroid.

Psoriasis image representation using patch-based dictionary learning for erythema severity scoring.

Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features. Secondly, the supervised phase is where various multi-class machine learning (ML) classifiers are trained for erythema severity scoring. Finally, we compare the proposed system with two popular unsupervised feature extractor methods, namely: bag of visual words model (BoVWs) and AlexNet pretrained model. Root mean square error (RMSE) and F1 score are used as performance measures for the learned dictionaries and the trained ML models, respectively. A psoriasis image set consisting of 676 images, is used in this study. Experimental results demonstrate that the use of the proposed procedure can provide a setup where erythema scoring is accurate and consistent. Also, it is revealed that dictionaries with large number of atoms and small patch sizes yield the best representative erythema severity features. Further, random forest (RF) outperforms other classifiers with F1 score 0.71, followed by support vector machine (SVM) and boosting with 0.66 and 0.64 scores, respectively. Furthermore, the conducted comparative studies confirm the effectiveness of the proposed approach with improvement of 9% and 12% over BoVWs and AlexNet based features, respectively.

Automatic psoriasis lesion segmentation in two-dimensional skin images using multiscale superpixel clustering.

Psoriasis is a chronic skin disease that is assessed visually by dermatologists. The Psoriasis Area and Severity Index (PASI) is the current gold standard used to measure lesion severity by evaluating four parameters, namely, area, erythema, scaliness, and thickness. In this context, psoriasis skin lesion segmentation is required as the basis for PASI scoring. An automatic lesion segmentation method by leveraging multiscale superpixels and [Formula: see text]-means clustering is outlined. Specifically, we apply a superpixel segmentation strategy on CIE-[Formula: see text] color space using different scales. Also, we suppress the superpixels that belong to nonskin areas. Once similar regions on different scales are obtained, the [Formula: see text]-means algorithm is used to cluster each superpixel scale separately into normal and lesion skin areas. Features from both [Formula: see text] and [Formula: see text] color bands are used in the clustering process. Furthermore, majority voting is performed to fuse the segmentation results from different scales to obtain the final output. The proposed method is extensively evaluated on a set of 457 psoriasis digital images, acquired from the Royal Melbourne Hospital, Melbourne, Australia. Experimental results have shown evidence that the method is very effective and efficient, even when applied to images containing hairy skin and diverse lesion size, shape, and severity. It has also been ascertained that CIE-[Formula: see text] outperforms other color spaces for psoriasis lesion analysis and segmentation. In addition, we use three evaluation metrics, namely, Dice coefficient, Jaccard index, and pixel accuracy where scores of 0.783%, 0.698%, and 86.99% have been achieved by the proposed method for the three metrics, respectively. Finally, compared with existing methods that employ either skin decomposition and support vector machine classifier or Euclidean distance in the hue-chrome plane, our multiscale superpixel-based method achieves markedly better performance with at least 20% accuracy enhancement.

Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis.

This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimised selection and integration of features derived from textural, borderbased and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a boundaryseries model of the lesion border and analysing it in spatial and frequency domains, and the geometry features are derived from shape indexes. The optimised selection of features is achieved by using the Gain-Ratio method, which is shown to be computationally efficient for melanoma diagnosis application. Classification is done through the use of four classifiers; namely, Support Vector Machine, Random Forest, Logistic Model Tree and Hidden Naive Bayes. The proposed diagnostic system is applied on a set of 289 dermoscopy images (114 malignant, 175 benign) partitioned into train, validation and test image sets. The system achieves and accuracy of 91.26% and AUC value of 0.937, when 23 features are used. Other important findings include (i) the clear advantage gained in complementing texture with border and geometry features, compared to using texture information only, and (ii) higher contribution of texture features than border-based features in the optimised feature set.

Optimized weighted performance index for objective evaluation of border-detection methods in dermoscopy images.

Quantitative evaluation of the existing border-detection methods is commonly performed by using different metrics. This is inherently problematic due to the different characteristics of each metric. This paper presents a novel approach for objective evaluation of border-detection methods in dermoscopy images by introducing a comprehensive evaluation metric: optimized weighted performance index. The index is formed as a nonlinear weighted function of the six commonly used metrics of sensitivity, specificity, accuracy, precision, border error, and similarity. Constrained nonlinear multivariable optimization techniques are applied to determine the optimal set of weights that result in the maximum value of the index. This index is used as an effective measure of the value of a given border-detection method and, thus, provides a basis for comparison with other methods. To demonstrate the effectiveness of the proposed index, it is used to evaluate five recent border-detection methods applied on a set of 55 high-resolution dermoscopy images.

Border detection in dermoscopy images using hybrid thresholding on optimized color channels.

Automated border detection is one of the most important steps in dermoscopy image analysis. Although numerous border detection methods have been developed, few studies have focused on determining the optimal color channels for border detection in dermoscopy images. This paper proposes an automatic border detection method which determines the optimal color channels and performs hybrid thresholding to detect the lesion borders. The color optimization process is tested on a set of 30 dermoscopy images with four sets of dermatologist-drawn borders used as the ground truth. The hybrid border detection method is tested on a set of 85 dermoscopy images with two sets of ground truth using various metrics including accuracy, precision, sensitivity, specificity, and border error. The proposed method, which is comprised of two stages, is designed to increase specificity in the first stage and sensitivity in the second stage. It is shown to be highly competitive with three state-of-the-art border detection methods and potentially faster, since it mainly involves scalar processing as opposed to vector processing performed in the other methods. Furthermore, it is shown that our method is as good as, and in some cases more effective than a dermatology registrar.

Weighted performance index for objective evaluation of border detection methods in dermoscopy images.

PURPOSE: This paper presents a novel approach for objective evaluation of border detection in dermoscopy images of melanoma. BACKGROUND: In melanoma studies, border detection is a fundamental step toward the development of a computer-aided diagnosis system. Therefore, its accuracy is essential for accurate implementation of the subsequent parts of the diagnostic system. METHOD: An objective evaluation procedure of border detection methods is presented. The evaluation procedure uses the weighted performance index, which is composed of weighted metrics of sensitivity, specificity, accuracy, precision, border error and similarity. This index can also be used to optimize the parameters of a border detection method. RESULT AND CONCLUSION: Experiments are performed on 55 high-resolution dermoscopy images. Using the union of four sets of dermatologist-drawn borders as the ground truth, weighted metrics of sensitivity, specificity, accuracy, precision, border error and similarity are evaluated. Then, the weighted performance index is constructed and used to optimize the parameters of the hybrid border detection method. The outcome of the optimization process, verified through statistical analysis, yields a higher degree of agreement between automatic borders and the ground truth, compared with using standard metrics only. Finally, the weighted performance index is used to evaluate five recently reported border detection methods.