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The distribution and amount of intramuscular fat and fibrous tissue can be influenced by biological sex and impact muscle quality in both the functional (force-generating capacity) and morphological (muscle composition) domains. While ultrasonography (US) has proven effective in assessing age- or sex-related differences in muscle quality, limited information is available on sex differences in children. Quantitative ultrasonographic measurements, such as echo intensity (EI), EI bands (number of pixels across 50-unit intervals) and texture, may offer a comprehensive framework for identifying sex differences in muscle composition. The aim of our study was to examine the effect of sex on the rectus femoris (RF) muscle quality in children. We used EI (mean and bands) and texture as muscle quality estimates derived from B-mode US. We hypothesised that RF muscle quality differs significantly between girls and boys. Additionally, we also hypothesised that there is a significant correlation between EI bands and texture. Forty-four non-active healthy children were recruited (n = 22 girls, 12.8 ± 1.5 years; and n = 22 boys, 13.5 ± 1.2 years). RF was assessed using EI mean, EI bands, and texture analysis (homogeneity and correlation) using the Gray-Level Co-Occurrence Matrix. The results revealed significant (p < 0.05) sex differences in RF EI bands and texture. Boys displayed higher values in the 0-50 EI band and had more homogeneous muscle texture than girls. Conversely, girls displayed greater values in the 51-100 EI band and had less homogenous texture compared to boys (p < 0.05). A positive correlation was observed between the 0-50 EI band and muscle homogeneity. However, the 51-100 EI band correlated negatively with homogeneity (p < 0.05), particularly for girls. In conclusion, our study revealed sex-specific differences in mean EI, EI bands, and texture of the RF muscle in children. The variations in the correlations between the first and second EI bands and texture reveal different levels of homogeneity in each band. This indicates that distinct muscle tissue constituents, such as intramuscular fat and/or connective tissue, may be reflected in EI bands. Overall, the methods used in this study may be useful for examining muscle quality in healthy children and those with medical conditions.

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The purpose of this study was to identify changes in the temporomandibular joint disc affected by effusion by using texture analysis of magnetic resonance images (MRIs). METHODS: A total of 223 images of the TMJ, 42 with joint effusion and 181 without, were analyzed. Three consecutive slices were then exported to MaZda software, in which two oval ROIs (one in the anterior band and another in the intermediate zone of the joint disc) were determined in each slice and eleven texture parameters were calculated by using a gray-level co-occurrence matrix. Spearman's correlation coefficient test was used to assess the correlation between texture variables and to select variables for analysis. The Mann-Whitney test was used to compare the groups. RESULTS: The significance level was set at 5%, with the results demonstrating that there was no high correlation between the parameter directions. It was possible to observe a trend between the average parameters, in which the group with effusion always had smaller values than the group without effusion, except for the parameter measuring the difference in entropy. CONCLUSION: The trend towards lower overall values for the texture parameters suggested a different behavior between TMJ discs affected by effusion and those not affected, indicating that there may be intrinsic changes.

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This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. METHODS: This retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL2) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI-ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS ≥ 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets. RESULTS: Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78). CONCLUSION: Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics' (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients.

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Fruit by-products are a valuable source of ingredients, in the formulation of what is known by "upcycled foods". Orange pomace, a by-product of orange juice industry, is a dietary fibre source. In this work, a powdered ingredient with soluble fibre obtained from orange pomace was used as replacement of inulin in the formulation of source of fibre "flan" like puddings. Four different formulations were analysed using Flash Profile and instrumental texture: 100% inulin, 70% inulin: 30% orange fibre, 30% inulin: 70% orange fibre, 100% orange fibre. The replacement of 30% of pudding's total fibre with the new ingredient helped to improve the texture and general appearance of the dessert. Greater percentages imparted non-desirable flavour attributes, such as bitterness and acidity. The use of this ingredient as a replacement of commercial inulin in the formulation of source of fibre puddings is possible. However, further research is needed to reduce the off flavours.

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OBJECTIVES: The present study aimed to evaluate the performance of QuantusFLM® software, which performs quantitative ultrasound analysis of fetal lung texture, in predicting lung maturity in fetuses of diabetic mothers. METHODS: The patients included in this study were between 34 and 38 weeks and 6 days gestation and were divided into two groups: (1) patients with diabetes on medication and (2) control. The ultrasound images were performed up to 48 h prior to delivery and analyzed using QuantusFLM® software, which classified each fetus as high or low risk for neonatal respiratory morbidity based on lung maturity or immaturity. RESULTS: A total of 111 patients were included in the study, being 55 in diabetes and 56 in control group. The pregnant women with diabetes had significantly higher body mass index (27.8 kg/m2 vs. 25.9 kg/m2, respectively, p=0.02), increased birth weight (3,135 g vs. 2,887 g, respectively, p=0.002), and a higher rate of labor induction (63.6 vs. 30.4 %, respectively, p<0.001) compared to the control group. QuantusFLM® software was able to predict lung maturity in diabetes group with 96.4 % accuracy, 96.4 % sensitivity and 100 % positive predictive value. Considering the total number of patients, the software demonstrated accuracy, sensitivity, specificity, positive predictive value and negative predictive value of 95.5 , 97.2, 33.3, 98.1 and 25 %, respectively. CONCLUSIONS: QuantusFLM® was an accurate method for predicting lung maturity in normal and DM singleton pregnancies and has the potential to aid in deciding the timing of delivery for pregnant women with DM.

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Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.

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OBJECTIVE: To define which are and how the radiomics features of jawbone pathologies are extracted for diagnosis, predicting prognosis and therapeutic response. METHODS: A comprehensive literature search was conducted using eight databases and gray literature. Two independent observers rated these articles according to exclusion and inclusion criteria. 23 papers were included to assess the radiomics features related to jawbone pathologies. Included studies were evaluated by using JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies. RESULTS: Agnostic features were mined from periapical, dental panoramic radiographs, cone beam CT, CT and MRI images of six different jawbone alterations. The most frequent features mined were texture-, shape- and intensity-based features. Only 13 studies described the machine learning step, and the best results were obtained with Support Vector Machine and random forest classifier. For osteoporosis diagnosis and classification, filtering, shape-based and Tamura texture features showed the best performance. For temporomandibular joint pathology, gray-level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), first-order statistics analysis and shape-based analysis showed the best results. Considering odontogenic and non-odontogenic cysts and tumors, contourlet and SPHARM features, first-order statistical features, GLRLM, GLCM had better indexes. For odontogenic cysts and granulomas, first-order statistical analysis showed better classification results. CONCLUSIONS: GLCM was the most frequent feature, followed by first-order statistics, and GLRLM features. No study reported predicting response, prognosis or therapeutic response, but instead diseases diagnosis or classification. Although the lack of standardization in the radiomics workflow of the included studies, texture analysis showed potential to contribute to radiologists' reports, decreasing the subjectivity and leading to personalized healthcare.

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Objective: the aim of this study was to analyse the performance of the technique of texture analysis (TA) with magnetic resonance imaging (MRI) scans of temporomandibular joints (TMJs) as a tool for identification of possible changes in individuals with migraine headache (MH) by relating the findings to the presence of internal derangements. Material and Methods: thirty MRI scans of the TMJ were selected for study, of which 15 were from individuals without MH or any other type of headache (control group) and 15 from those diagnosed with migraine. T2-weighted MRI scans of the articular joints taken in closed-mouth position were used for TA. The co-occurrence matrix was used to calculate the texture parameters. Fisher's exact test was used to compare the groups for gender, disc function and disc position, whereas Mann-Whitney's test was used for other parameters. The relationship of TA with disc position and function was assessed by using logistic regression adjusted for side and group. Results: the results indicated that the MRI texture analysis of articular discs in individuals with migraine headache has the potential to determine the behaviour of disc derangements, in which high values of contrast, low values of entropy and their correlation can correspond to displacements and tendency for non-reduction of the disc in these individuals. Conclusion: the TA of articular discs in individuals with MH has the potential to determine the behaviour of disc derangements based on high values of contrast and low values of entropy (AU)

Objetivo: o objetivo deste estudo foi analisar o desempenho da técnica de análise de textura (AT) em exames de ressonância magnética (RM) das articulações temporomandibulares (ATM) como ferramenta para identificação de possíveis alterações em indivíduos com cefaléia migrânea (CM) relacionando os achados com a presença de desarranjos internos. Material e Métodos: trinta exames de RM das ATM foram selecionados para estudo, sendo 15 de indivíduos sem cefaleia migrânea ou qualquer outro tipo de cefaléia (grupo controle) e 15 diagnosticados com CM. As imagens de RM ponderadas em T2 das articulações realizadas na posição de boca fechada foram usadas para AT. A matriz de co-ocorrência foi usada para calcular os parâmetros de textura. O teste exato de Fisher foi usado para comparar os grupos quanto ao sexo, função do disco e posição do disco, enquanto o teste de Mann-Whitney foi usado para os demais parâmetros. A relação da AT com a posição e função do disco foi avaliada por meio de regressão logística ajustada para lado e grupo. Resultados: a AT por RM dos discos articulares em indivíduos com cefaleia migrânea tem o potencial de determinar o comportamento dos desarranjos discais, em que altos valores de contraste, baixos valores de entropia e sua correlação podem corresponder a deslocamentos e tendência a não redução do disco nesses indivíduos. Conclusão: a análise de textura dos discos articulares em indivíduos com CM tem potencial para determinar o comportamento dos desarranjos do disco com base em altos valores de contraste e baixos valores de entropia. (AU)

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This study analyzes the effectivity of a freeze-dried additive formulated with inulin (I), Stevia (St), and ultrafiltered bovine plasma proteins (P) as a sugar substitute on the final properties of a sugar-free and low-fat muffins formulation. The following analysis were performed: shape factor, moisture loss, lamella thickness, final volume, aeration, pore size distribution and textural analysis. The addition of the binary combination 50%(P + St + I) + 50%(Sucralose) generated a synergistic effect: increasing the shape factor, final volume and aeration, and improving the pore size distribution and moisture loss. Given the success, the concentration of (P + St + I) was adjusted. A 12.5% concentration of (P + St + I) generated a hardness decrease during the studied period and did not exhibit statistical significant differences when compared to the control sample. Therefore this study demonstrated the effectiveness of the combination of Stevia, inulin, and bovine plasma proteins as sugar substitute.

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Several studies have demonstrated statistical and texture analysis abilities to differentiate cancerous from healthy tissue in magnetic resonance imaging. This study developed a method based on texture analysis and machine learning to differentiate prostate findings. Forty-eight male patients with PI-RADS classification and subsequent radical prostatectomy histopathological analysis were used as gold standard. Experienced radiologists delimited the regions of interest in magnetic resonance images. Six different groups of images were used to perform multiple analyses (seven analyses variations). Those analyses were outlined by specialists in urology as those of most significant importance for the classification. Forty texture features were extracted from each image and processed with Random Forest, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes. Those seven analyses variation results were described in terms of area under the ROC curve (AUC), accuracy, F-score, precision and sensitivity. The highest AUC (93.7%) and accuracy (88.8%) were obtained when differentiating the group with both MRI and histopathology positive findings against the group with both negative MRI and histopathology. When differentiating the group with both MRI and histopathology positive findings versus the peripheral image zone group the AUC value was 86.6%. When differentiating the group with negative MRI/positive histopathology versus the group with both negative MRI and histopathology the AUC value was 80.7%. The evaluation of statistical and texture analysis promoted very suggestive indications for future work in prostate cancer suspicious regions. The method is fast for both region of interest selection and classification with machine learning and the result brings original contributions in the classification of different groups of patients. This tool is low-cost, and can be used to assist diagnostic decisions.

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BACKGROUND: The initial contact of consumers when choosing sunscreens is through the trademark, packaging, perfume, and tactile feeling of the product, outlining the popular practice of sensory science. AIMS: To describe the sensory and physical-mechanical profile of commercial sunscreens through sensory and instrumental analyses related to principal component analysis (PCA). METHODS: Seven commercial sunscreens available on the Brazilian market and with a solar protection factor (SPF) of 30 were evaluated. Physical-mechanical profiling (rheological and textural analyses) was conducted by a trained panel, followed by sensory profile characterization and descriptive analysis. The results were assessed using the Spearman correlation coefficient and PCA. RESULTS: The correlation or lack thereof of the instrumental parameters with most of the sensory aspects was demonstrated using the Spearman coefficient. PCA enabled us to identify the nature of the dissimilarities among the samples. CONCLUSIONS: The obtained results highlight the importance of descriptive sensory analysis in the research and development of sunscreens, evidencing the significance of precisely informing the products' formulation in order to be chosen by the consumer.

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Las aplicaciones de análisis de texturas y su extracción de características son consideradas tendencias de investigación en las neurociencias. La textura como método de análisis de imágenes ha mostrado resultados prometedores en la detección de lesiones visibles y no visibles, y en estudios de tomografía computarizada (TC) son escasos. La presente investigación tiene como objetivo determinar la aplicabilidad del procesamiento automático de índices de texturas homogéneas en la estimación volumétrica de la sustancia gris cerebral en imágenes de TC craneal. Para ello se utilizaron imágenes artificiales con regiones predefinidas y la selección de imágenes de TC en los pacientes con indicaciones previas de TC de cráneo. Dos pasos fundamentales son conducidos para la implementación de este enfoque. Como resultado se obtuvo un método automático de reconocimiento de patrones sin ventanas por medio de la extracción de características de textura homogéneas a través de la matriz de co-ocurrencia(AU)

Texture analysis applications and their extraction of features are considered research trends in neuroscience. Texture as a method of image analysis has shown promising results in the detection of visible and non-visible lesions, and in computed tomography (CT) studies they are scarce. The present research aims to determine the applicability of the automatic processing of homogeneous texture indices in the volumetric estimation of brain gray matter in cranial CT images. For this, artificial images with predefined regions and the selection of CT images were used in patients with previous indications for CT of the skull. Two fundamental steps are taken for the implementation of this approach. As a result, an automatic windowless pattern recognition method was obtained by means of the extraction of homogeneous texture characteristics through the co-occurrence matrix(AU)

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The quality control of medicines guarantees the effectiveness of treatments for diseases. We explore the use of texture analysis of patterns in dried droplets as a tool to readily detect both impurities and changes in drug concentration. Four types of medicines associated with different routes of administration were analyzed: Methotrexate, Ciprofloxacin, Clonazepam, and Budesonide. We use NaCl and a hot substrate at 63 ∘C to promote aggregate formation and to reduce droplet drying time. Depending on the medicine, optical microscopy reveals different complex aggregates such as circular to oval splatters, fern-like islands, crown shapes, crown needle-like and bump-like patterns as well as dendritic branched and star-like crystals. We use some physical features of the stains (as the stain diameter and superficial area) and gray level co-occurrence matrix (GLCM) to characterize patterns of dried droplets. Finally, we show that structural analysis of stains can achieve 95% accuracy in identifying medicines with 30% water dilution, while it achieves 99% accuracy in detecting drugs with 10% other substances.

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COVID-19 is an infectious disease caused by a newly discovered type of coronavirus called SARS-CoV-2. Since the discovery of this disease in late 2019, COVID-19 has become a worldwide concern, mainly due to its high degree of contagion. As of April 2021, the number of confirmed cases of COVID-19 reported to the World Health Organization has already exceeded 135 million worldwide, while the number of deaths exceeds 2.9 million. Due to the impacts of the disease, efforts in the literature have intensified in terms of studying approaches aiming to detect COVID-19, with a focus on supporting and facilitating the process of disease diagnosis. This work proposes the application of texture descriptors based on phylogenetic relationships between species to characterize segmented CT volumes, and the subsequent classification of regions into COVID-19, solid lesion or healthy tissue. To evaluate our method, we use images from three different datasets. The results are promising, with an accuracy of 99.93%, a recall of 99.93%, a precision of 99.93%, an F1-score of 99.93%, and an AUC of 0.997. We present a robust, simple, and efficient method that can be easily applied to 2D and/or 3D images without limitations on their dimensionality.

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BACKGROUND: Platelet-rich plasma (PRP) has been used to favor anterior cruciate ligament (ACL) healing after reconstruction surgeries. However, clinical data are still inconclusive and subjective about PRP. Thus, we propose a quantitative method to demonstrate that PRP produced morphological structure changes. METHODS: Thirty-four patients undergoing ACL reconstruction surgery were evaluated and divided into control group (sixteen patients) without PRP application and experiment group (eighteen patients) with intraoperative application of PRP. Magnetic resonance imaging (MRI) scans were performed 3 months after surgery. We used Matlab® and machine learning (ML) in Orange Canvas® to texture analysis (TA) features extraction. Experienced radiologists delimited the regions of interest (RoIs) in the T2-weighted images. Sixty-two texture parameters were extracted, including gray-level co-occurrence matrix and gray level run length. We used the algorithms logistic regression (LR), naive Bayes (NB), and stochastic gradient descent (SGD). RESULTS: The accuracy of the classification with NB, LR, and SGD was 83.3%, 75%, 75%, respectively. For the area under the curve, NB, LR, and SGD presented values of 91.7%, 94.4%, 75%, respectively. In clinical evaluations, the groups show similar responses in terms of improvement in pain and increase in the IKDC index (International Knee Documentation Committee) and Lysholm score indices differing only in the assessment of flexion, which presents a significant difference for the group treated with PRP. CONCLUSIONS: Here, we demonstrated quantitatively that patients who received PRP presented texture changes when compared to the control group. Thus, our findings suggest that PRP interferes with morphological parameters of the ACL. TRIAL REGISTRATION: Protocol no. CAAE 56164316.6.0000.5411.

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Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder-decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.

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BACKGROUND: Reliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates. RESULTS: Correcting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R2 = 0.44 vs R2 = 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R2 = 0.26). CONCLUSIONS: This study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

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BACKGROUND: The aim of this study was to apply texture analysis (TA) to cone-beam computed tomography (CBCT) scans of patients with grade C periodontitis for detection of non-visible changes in the image. METHODS: TA was performed on CBCT scans of 34 patients with grade C periodontitis. Axial sections of CBCT were divided into three groups as follows: Group L (lesion) in which there is a furcal lesion with periodontal bone loss; Group I (intermediate) in which the border of the furcal lesion has normal characteristics; and Group C (control) in which the area is healthy. Eleven texture parameters were extracted from the region of interest. Mann-Whitney U test was used to assess the differences in the texture between the three groups as follows: L versus I; L versus C, and I versus C. RESULTS: Statistically significant differences (P <0.05) were observed in almost all parameters in the intergroup analyses (i.e., L versus I and L versus C). However, statistical differences were smaller in groups I versus C in which only entropy of sum, entropy of difference, mean of sum, and variance of difference were statistically different (P < 0.05). CONCLUSION: TA can potentially provide prognostic information to improve the diagnostic accuracy in the grading of the tissue around the furcal lesion, thus potentially accelerating the treatment decision-making process.

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PURPOSE: To design a multiscale descriptor capable of capturing complex local-regional unfolding patterns to support quantitation and diagnosis of autism spectrum disorders (ASD) using T1-weighted structural magnetic resonance images (MRI) with voxel size of 1 × 1 × 1 mm. METHODS: The proposed image descriptor uses an adapted multiscale representation, the Curvelet transform, interpretable in terms of texture (local) and shape (regional) to characterize brain regions, and a Generalized Gaussian Distribution (GGD) to reduce feature dimensionality. In this approach, each MRI is first parcelled into 3D anatomical regions. Each resultant region is represented by a single 2D image where slices are placed next to each other. Each 2D image is characterized by mapping it to the Curvelet space and each of the different Curvelet sub-bands is described by the set of GGD parameters. To assess the discriminant power of the proposed descriptor, a classification model per brain region was built to differentiate ASD patients from control subjects. Models were constructed with support vector machines and evaluated using two samples from heterogeneous databases, namely Autism Brain Imaging Data Exchange - ABIDE I (34 ASD and 34 controls, mean age 11.46 ± 2.03 and 11.53 ± 1.79 yr, respectively, male population) and ABIDE II (42 ASD and 41 controls, mean age 10.09 ± 1.37 and 10.52 ± 1.27 yr, respectively, male population), for a total of 151 individuals. RESULTS: When the model was trained with ABIDE II sample and tested with ABIDE I on a hold-out validation, an area under receiver operator curve (AUC) of 0.69 was computed. When each sample was independently used under a cross-validation scheme, the estimated AUC was 0.75 ± 0.02 for ABIDE I and 0.77 ± 0.01 for ABIDE II. This analysis determined a set of discriminant regions widely reported in the literature as characteristic of ASD. CONCLUSIONS: The presented image descriptor demonstrated differences at local and regional level when high differences were observed in the Curvelet sub-bands. The method is simple in conceptual terms, robust to several sources of noise, and has a very low computational cost.

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Medical images have made a great contribution to early diagnosis. In this study, a new strategy is presented for analyzing medical images of skin with melanoma and nevus to model, classify and identify lesions on the skin. Machine learning applied to the data generated by first and second order statistics features, Gray Level Co-occurrence Matrix (GLCM), keypoints and color channel information-Red, Green, Blue and grayscale images of the skin were used to characterize decisive information for the classification of the images. This work proposes a strategy for the analysis of skin images, aiming to choose the best mathematical classifier model, for the identification of melanoma, with the objective of assisting the dermatologist in the identification of melanomas, especially towards an early diagnosis.