scholarly journals Extração semiautomática de vegetação arbórea em áreas urbanas a partir de imagens aéreas de alta resolução espacial

2020 ◽  
Vol 13 (3) ◽  
pp. 1391
Author(s):  
Jakeline Jesus Silva ◽  
Lucas Prado Osco ◽  
Ana Paula Marques Ramos ◽  
Wesley Barbosa Dourado
Keyword(s):  
Receiver Operating Characteristic ◽  
Spatial Resolution ◽  
Roc Curve ◽  
Urban Areas ◽  
Operating Characteristic ◽  
High Spatial Resolution ◽  
Confusion Matrix ◽  
Computational Cost ◽  
Area Under The Curve ◽  
Receiver Operating

O mapeamento da vegetação arbórea em áreas urbanas pode ser realizado por classificação semiautomática ou automática de imagens orbitais ou aéreas. Contudo, esse tipo de tarefa tem um custo computacional dependente da resolução espacial da imagem. Neste estudo é proposto uma abordagem de extração semiautomática de vegetação arbórea em imagens de alta resolução espacial a baixo custo computacional. Trabalhamos com ortofotos de 1m de resolução, disponibilizadas por órgãos gestores públicos. A abordagem proposta aplica um filtro de médias em recortes de imagens, com 500x500 pixels cada. Ao todo utilizamos 90 recortes. Testamos o algoritmo nas seguintes configurações: separadamente nas bandas (azul, verde e vermelho), em imagem colorida (RGB) e em imagem em tons de cinza. Validamos sua performance usando a matriz de confusão e a curva do Receiver Operating Characteristic (ROC), considerando 3.695 pontos distribuídos homogeneamente em todos os recortes de imagens. Comparamos, ainda, a performance do algoritmo com uma classificação supervisionado por pixel (máxima verossimilhança). Obtivemos uma acurácia global de 90,18%, um índice kappa de 0,80 e uma velocidade de processamento de aproximadamente 1 minuto e 30 segundos para o algoritmo proposto em um computador convencional. A curva ROC obteve uma Area Under the Curve (AUC) equivalente a 0,91 para o algoritmo, considerando o resultado de todas as bandas, e um valor de 0,79 para a classificação supervisionada por pixel. Concluímos que nossa abordagem é computacionalmente eficiente para separar as áreas cobertas por vegetação de áreas não cobertas em ambiente urbano. Semiautomatic extraction of arboreal vegetation in urban areas using aerial imagery of high spatial resolution A B S T R A C TMapping of tree vegetation in urban areas can be performed by semi-automatic or automatic classification of orbital or aerial images. However, this type of task has a computational cost dependent on the spatial resolution of the image. This study proposes an approach of semi-automatic tree vegetation extraction in high spatial resolution images at a low computational cost. We work with 1m resolution orthophotos, made available by public management agencies. The proposed approach applies a medium filter on image clippings of 500x500 pixels each. In all, we use 90 clippings. We tested the algorithm in the following configurations: separately in the bands (blue, green and red), color image (RGB) and grayscale image. We validated its performance using the Confusion Matrix and Receiver Operating Characteristic (ROC) curve, considering 3,695 points evenly distributed across all clippings. We also compared the performance of the algorithm with a pixel supervised classification (maximum likelihood). We obtained an overall accuracy of 90.18%, a kappa index of 0.80 and a processing speed of approximately 1 minute and 30 seconds for the proposed algorithm in a conventional computer. The ROC curve obtained an Area Under the Curve (AUC) equivalent to 0.91 for the algorithm, considering the result of all bands, and a value of 0.79 for the supervised pixel classification. We conclude that our approach is computationally efficient for separating areas covered by vegetation from areas not covered in an urban environment.Keywords: digital image processing; image classification; urban environmental planning.

scholarly journals Η άλφα1-μικροσφαιρίνη ως δείκτης πρώιμης νεφρικής βλάβης σε βαρέως πάσχοντες σηπτικούς ασθενείς της Μ.Ε.Θ.

2015 ◽  
Author(s):  
Ειρήνη Τερζή
Keyword(s):  
Acute Kidney Injury ◽  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Kidney Injury ◽  
Apache Ii ◽  
Receiver Operating

Μελετήθηκε η συμβολή της άλφα1-μικροσφαιρίνης (alpha1-microglobulin, α1M) - ενός μέλους της οικογένειας των λιποκαλινών, που αποτελεί δείκτη εγγύς νεφροσωληναριακής δυσλειτουργίας - στην πρώιμη διαγνωστική της σχετιζόμενης με την σήψη οξείας νεφρικής βλάβης (acute kidney injury, AKI). Η μελέτη επικεντρώθηκε σε βαρέως πάσχοντες ασθενείς μιας πολυδύναμης Μονάδας Εντατικής Θεραπείας (Μ.Ε.Θ.). Από την προοπτική παρακολούθηση 290 ασθενών που εισήχθησαν για νοσηλεία σε διάστημα ενός έτους, μελετήθηκαν 45 σηπτικοί ασθενείς, εκ των οποίων οι 16 (35.6%) εκδήλωσαν νεφρική ανεπάρκεια. Η α1Μ προσδιορίσθηκε σε δείγματα ούρων από συλλογές ούρων 24ώρου κατά το σηπτικό επεισόδιο και σε συγκεκριμένα χρονικά διαστήματα έκτοτε. Η διαγνωστική ικανότητα του βιοδείκτη εκτιμήθηκε με τον μη παραμετρικό υπολογισμό της περιοχής κάτω από την καμπύλη μίας καμπύλης λειτουργικού χαρακτηριστικού δέκτη (area under the curve (AUC) of the receiver operating characteristic (ROC) curve, AUCROC). Τα επίπεδα της α1Μ ήταν σημαντικά υψηλότερα σε όλους τους σηπτικούς ασθενείς (μέση τιμή επιπέδων σε όλα τα δείγματα στο σηπτικό επεισόδιο 46.02 ± 7.17 mg/l) και παρουσίασαν αυξητική τάση στους ασθενείς που τελικά ανέπτυξαν σηπτική νεφρική ανεπάρκεια. Η AUCROC για την πρόβλεψη της σηπτικής ΑKΙ σύμφωνα με τα επίπεδα της α1M 24 ώρες πριν την εμφάνιση της νεφρικής προσβολής ήταν 0.739 (ευαισθησία 87.5%, ειδικότητα 62.07%, τιμή-όριο 47.9 mg/l). Τα επίπεδα της α1Μ 24 ώρες πριν την σηπτική νεφρική προσβολή, η κρεατινίνη ορού και η βαθμολογία βαρύτητας νόσου κατά APACHE II στο επεισόδιο της σήψης, αναδείχθηκαν ως οι σημαντικότεροι ανεξάρτητοι προγνωστικοί παράγοντες πρόβλεψης της ΑΚΙ. Ο συνδυασμός των ανωτέρω τριών παραμέτρων βελτίωσε την AUCROC της πρόγνωση της AKI σε 0.944. Τα αποτελέσματα υποστηρίζουν την ιδέα πως τα επίπεδα της α1Μ στα ούρα θα μπορούσαν να συμβάλουν στην πρώιμη διάκριση των σηπτικών ασθενών που εξελίσσονται σε ΑΚΙ και μπορεί να αποδειχθούν χρήσιμος βιοδείκτης. Παράλληλα, αναδεικνύουν ως θέμα για περαιτέρω έρευνα την παθογενετική εμπλοκή της α1Μ στην σήψη και στην σηπτική ΑΚΙ.

The area under the generalized receiver-operating characteristic curve

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Pablo Martínez-Camblor ◽  
Sonia Pérez-Fernández ◽  
Susana Díaz-Coto
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Asymptotic Variance ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Finite Samples ◽  
Real World Problem ◽  
Graphical Tool ◽  
Receiver Operating

Abstract The receiver operating-characteristic (ROC) curve is a well-known graphical tool routinely used for evaluating the discriminatory ability of continuous markers, referring to a binary characteristic. The area under the curve (AUC) has been proposed as a summarized accuracy index. Higher values of the marker are usually associated with higher probabilities of having the characteristic under study. However, there are other situations where both, higher and lower marker scores, are associated with a positive result. The generalized ROC (gROC) curve has been proposed as a proper extension of the ROC curve to fit these situations. Of course, the corresponding area under the gROC curve, gAUC, has also been introduced as a global measure of the classification capacity. In this paper, we study in deep the gAUC properties. The weak convergence of its empirical estimator is provided while deriving an explicit and useful expression for the asymptotic variance. We also obtain the expression for the asymptotic covariance of related gAUCs and propose a non-parametric procedure to compare them. The finite-samples behavior is studied through Monte Carlo simulations under different scenarios, presenting a real-world problem in order to illustrate its practical application. The R code functions implementing the procedures are provided as Supplementary Material.

scholarly journals RGB Pixel Brightness Characteristics of Linked Color Imaging in Early Gastric Cancer: A Pilot Study

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xue Sun ◽  
Li Zhao
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Color Imaging ◽  
Linked Color Imaging ◽  
Pixel Brightness ◽  
Receiver Operating

Background and Aims. Linked color imaging (LCI) helps screen and diagnose for early gastric cancer by color contrast in different mucosa. RGB (red, green, and blue) pixel brightness quantifies colors, which is relatively objective. Limited studies have combined LCI images with RGB to help screen for early gastric cancer (EGC). We aimed to evaluate the RGB pixel brightness characteristics of EGC and noncancer areas in LCI images. Methods. We retrospectively reviewed early gastric cancer (EGC) patients and LCI images. All pictures were evaluated by at least two endoscopic physicians. RGB pixel brightness analysis of LCI images was performed in MATLAB software to compare the cancer with noncancer areas. Receiver operating characteristic (ROC) curve was analyzed for sensitivity, specificity, cut-off, and area under the curve (AUC). Results. Overall, 38 early gastric cancer patients were enrolled with 38 LCI images. Pixel brightness of red, green, and blue in cancer was remarkably higher than those in noncancer areas (190.24±37.10 vs. 160.00±40.35, p<0.001; 117.96±33.91 vs. 105.33±30.01, p=0.039; 114.36±34.88 vs. 90.93±30.14, p<0.001, respectively). Helicobacter plyori (Hp) infection was not relevant to RGB distribution of EGC. Whether the score of Kyoto Classification of Gastritis (KCG) is ≥4 or <4, the pixel brightness of red, green, and blue was not disturbed in both cancer and noncancer (p>0.05). Receiver operating characteristic (ROC) curve for differentiating cancer from noncancer was calculated. The maximum area under the curve (AUC) was 0.767 in B/G, with a sensitivity of 0.605, a specificity of 0.921, and a cut-off of 0.97. Conclusions. RGB pixel brightness was useful and more objective in distinguishing early gastric cancer for LCI images.

Two-stage receiver operating-characteristic curve estimator for cohort studies

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Susana Díaz-Coto ◽  
Norberto Octavio Corral-Blanco ◽  
Pablo Martínez-Camblor
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Practical Implementation ◽  
Two Stage ◽  
Statistical Tool ◽  
Finite Samples ◽  
Receiver Operating

AbstractThe receiver operating-characteristic (ROC) curve is a graphical statistical tool routinely used for studying the classification accuracy in both, diagnostic and prognosis problems. Given the different nature of these situations, ROC curve estimation has been separately considered for binary (diagnostic) and time-to-event (prognosis) outcomes, even for data coming from the same study design. In this work, the authors propose a two-stage ROC curve estimator which allows to link both contexts through a general prediction model (first-stage) and the empirical cumulative estimator of the distribution function (second-stage) of the considered test (marker) on the total population. The so-called two-stage Mixed-Subject (sMS) approach proves its behavior on both, large-samples (theoretically) and finite-samples (via Monte Carlo simulations). Besides, a useful asymptotic distribution for the concomitant area under the curve is also computed. Results show the ability of the proposed estimator to fit non-standard situations by considering flexible predictive models. Two real-world examples, one with binary and one with time-dependent outcomes, help us to a better understanding of the proposed methodology on usual practical circumstances. The R code used for the practical implementation of the proposed methodology and its documentation is provided as supplementary material.

scholarly journals Identification of an optimal prolactin threshold to determine prolactinoma size using receiver operating characteristic analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bianca M. Leca ◽  
Maria Mytilinaiou ◽  
Marina Tsoli ◽  
Andreea Epure ◽  
Simon J. B. Aylwin ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Tumour Size ◽  
Area Under The Curve ◽  
Serum Prolactin ◽  
Pituitary Neoplasms ◽  
Characteristic Analysis ◽  
Roc Curve Analysis ◽  
Baseline Serum ◽  
Receiver Operating

AbstractProlactinomas represent the most common type of secretory pituitary neoplasms, with a therapeutic management that varies considerably based on tumour size and degree of hyperprolactinemia. The aim of the current study was to evaluate the relationship between serum prolactin (PRL) concentrations and prolactinoma size, and to determine a cut-off PRL value that could differentiate micro- from macro-prolactinomas. A retrospective cohort study of 114 patients diagnosed with prolactinomas between 2007 and 2017 was conducted. All patients underwent gadolinium enhanced pituitary MRI and receiver operating characteristic (ROC) analyses were performed. 51.8% of patients in this study were men, with a mean age at the time of diagnosis of 42.32 ± 15.04 years. 48.2% of the total cohort were found to have microadenomas. Baseline serum PRL concentrations were strongly correlated to tumour dimension (r = 0.750, p = 0.001). When performing the ROC curve analysis, the area under the curve was 0.976, indicating an excellent accuracy of the diagnostic method. For a value of 204 μg/L (4338 mU/L), sensitivity and specificity were calculated at 0.932 and 0.891, respectively. When a cut off value of 204 μg/L (4338 mU/L) was used, specificity was 93.2%, and sensitivity 89.1%, acceptable to reliably differentiate between micro- and macro- adenomas.

Risikoabschätzung einer akuten Exazerbation bei COPD-Patienten im Rahmen einer pneumologischen Anschluss-Rehabilitation anhand der Prävalenz und Schwergradausprägung von Komorbiditäten

Pneumologie ◽  
2021 ◽  
Author(s):  
P. Luu ◽  
S. Tulka ◽  
S. Knippschild ◽  
W. Windisch ◽  
M. Spielmanns
Keyword(s):  
Receiver Operating Characteristic ◽  
Charlson Comorbidity Index ◽  
Operating Characteristic ◽  
Rating Scale ◽  
Area Under The Curve ◽  
Sensitivität Und Spezifität ◽  
Comorbidity Index ◽  
Receiver Operating

Zusammenfassung Einleitung Akute COPD-Exazerbationen (AECOPD) im Rahmen einer pneumologischen Rehabilitation (PR) sind häufige und gefährliche Komplikationen. Neben Einschränkungen der Lebensqualität führen sie zu einem Unterbrechung der PR und gefährden den PR-Erfolg. Eine Abhängigkeit zwischen dem Krankheitsstatus und einem erhöhten Risiko für eine AECOPD ist beschrieben. Dabei stellt sich die Frage, ob der Charlson Comorbidity Index (CCI) oder die Cumulative Illness Rating Scale (CIRS) dafür geeignet sind, besonders exazerbationsgefährdete COPD-Patienten in der PR im Vorfeld zu detektieren. Patienten und Methoden In einer retrospektiven Untersuchung wurden die Daten von COPD-Patienten, welche im Jahr 2018 eine PR erhielten, analysiert. Primärer Endpunkt der Untersuchung war die Punktzahl im CCI. Alle Daten wurden dem Klinikinformationssystem Phönix entnommen und COPD-Exazerbationen erfasst. Die laut Fallzahlplanung benötigten 44 Patienten wurden zufällig (mittels Zufallsliste für jede Gruppe) aus diesem Datenpool rekrutiert: 22 Patienten mit und 22 ohne Exazerbation während der PR. CCI und CIRS wurden für die eingeschlossenen Fälle für beide Gruppen bestimmt. Die Auswertung des primären Endpunktes (CCI) erfolgte durch den Gruppenvergleich der arithmetischen Mittel und der Signifikanzprüfung (Welch-Tests). Weitere statistische Lage- und Streuungsmaße wurden ergänzt (Median, Quartile, Standardabweichung).Zusätzlich wurde mittels Receiver Operating Characteristic (ROC)-Analyse sowohl für den CCI als auch für den CIRS ein optimaler Cutpoint zur Diskriminierung in AECOPD- und Nicht-AECOPD-Patienten gesucht. Ergebnisse 244 COPD-Patienten erhielten eine stationäre PR von durchschnittlich 21 Tagen, wovon 59 (24 %) während der PR eine behandlungspflichtige AECOPD erlitten. Die ausgewählten 22 Patienten mit einer AECOPD hatten einen mittleren CCI von 6,77 (SD: 1,97) und die 22 Patienten ohne AECOPD von 4,32 (SD: 1,17). Die Differenz von –2,45 war zu einem Signifikanzniveau von 5 % statistisch signifikant (p < 0,001; 95 %-KI: [–3,45 ; –1,46]). Die ROC-Analyse zeigte einen optimalen Cutpoint für den CCI bei 6 mit einer Sensitivität zur Feststellung einer AECOPD von 81,8 % und einer Spezifität von 86.,4 % mit einem Wert der AUC (area under the curve) von 0,87. Der optimale Cutpoint für den CIRS war 19 mit einer Sensitivität von 50 %, einer Spezifität von 77,2 % und einer AUC von 0,65. Schlussfolgerung COPD-Patienten mit einer akuten Exazerbation während der pneumologischen Rehabilitation haben einen höheren CCI. Mithilfe des CCI lässt sich mit einer hohen Sensitivität und Spezifität das Risiko einer AECOPD von COPD-Patienten im Rahmen eines stationären PR-Programms einschätzen.

Considerations on the region of interest in the ROC space

2021 ◽  
pp. 096228022110605
Author(s):  
Luigi Lavazza ◽  
Sandro Morasca
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
True Positive Rate ◽  
True Positive ◽  
Receiver Operating Characteristic Curves ◽  
Positive Rate ◽  
Receiver Operating

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

scholarly journals miR-142-3p as a novel biomarker for predicting poor prognosis in renal cell carcinoma patients after surgery

2019 ◽  
Vol 34 (3) ◽  
pp. 302-308 ◽  
Author(s):  
Xiqi Peng ◽  
Xiang Pan ◽  
Kaihao Liu ◽  
Chunduo Zhang ◽  
Liwen Zhao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Receiver Operating Characteristic ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Receiver Operating

Background: miR-142-3p has proved to be involved in tumorigenesis and the development of renal cell carcinoma. The present study aimed to explore the prognostic value of miR-142-3p. Methods: Total RNA was extracted from renal cell carcinoma specimens and the expression level of miR-142-3p was measured. Pearson Chi-square test, Kaplan–Meier analysis, as well as univariate and multivariate regression analysis were performed to determine the correlation between miR-142-3p and the prognosis of renal cell carcinoma patients. Receiver operating characteristic curves were constructed to evaluate the predictive efficiency of miR-142-3p for the prognosis of renal cell carcinoma patients. Data from The Cancer Genome Atlas (TCGA) were utilized to validate our findings. Results: Our results demonstrated that upregulation of miR-142-3p was correlated with shorter overall survival (P=0.002) and was, in the meantime, an independent prognostic factor for renal cell carcinoma patients (P=0.002). The receiver operating characteristic curve combining miR-142-3p expression with tumor stage showed an area under the curve of 0.633 (95% confidence interval 0.563, 0.702). The result of TCGA data was consistent with our findings. Conclusions: Our results suggest miR-142-3p expression is correlated with poor prognosis of renal cell carcinoma patients and may serve as a prognostic biomarker in the future.

Sign in / Sign up

Export Citation Format

Share Document