ABSTRACT
El presente trabajo tiene el propósito de analizar, para el caso particular de imágenes de microscopía celular de eritrocitos de sangre humana, en qué medida puede influir en la calidad de la imagen la aplicación de métodos de interpolación para mejorar la resolución de esta y con qué métodos se podrían obtener resultados satisfactorios. Se aplicaron para su comparación tres métodos de interpolación a las imágenes a color seleccionadas: splines cúbicos, bicúbico y bilineal y se evaluó la eficiencia computacional de estos. Se utilizaron dos factores de reducción de la resolución (2 y 4) por filas y columnas de la imagen digital. Las medidas utilizadas para valorar la calidad de las imágenes interpoladas fueron la relación señal-ruido y el error medio cuadrático, cuyos valores fueron procesados estadísticamente mediante las pruebas de Friedman y de Wilcoxon, esta última como test post-hoc. Los resultados permiten recomendar el método bicúbico de interpolación como el más favorable para este tipo de imágenes ya que fue el de mejor comportamiento entre los utilizados(AU)
The present work has the purpose of analyzing, for the particular case of cell microscopy images of erythrocytes from human blood, to what extent the application of interpolation methods to improve the image resolution can influence the image quality and with which methods satisfactory results might be obtained. Three interpolation methods were applied for their comparison to the selected color images: cubic splines, bicubic and bilinear and their computational efficiency was also evaluated. Two resolution reduction factors (2 and 4) were used for rows and columns of the digital image. The measures used to assess the quality of the interpolated images were the signal-to-noise ratio and the mean square error, whose values were statistically processed using the Friedman and Wilcoxon tests, the latter as a post-hoc test. The results make it possible to recommend the bicubic interpolation method as the most favorable for this type of images since it was the one with the best performance among those used(AU)
Subject(s)
Humans , Male , Female , Medical Informatics Applications , Microscopy/methodsABSTRACT
Abstract in Brazil, management agricultural practices not currently consider the soil spatial variability as a result, crop growth can be non-uniform and yields often is low. This research aims to compare Kriging, Cokriging and Collocated cokriging using soil physical and hydraulic properties and their influences on soybean development. We hypothesized that spatial variability of physical and hydraulic properties has influence on soybean development and this variability can be better represented by Collocated Cokriging method. To test these hypotheses, we accessed the soil physical and hydraulic attributes in a field experiment under no-till system, cultivated with soybean. Geostatistical interpolators were applied to generate maps from which spatial dependence of the variables was evaluated. The experiment was conducted on a sandy clay loam Oxisol, on an experimental station located in Ponta Grossa, Paraná, Brazil. Evaluation of the soil attributes was performed: bulk density (BD), particle size distribution, saturated soil hydraulic conductivity (K fs ), total porosity (TP), macroporosity and microporosity. The plant was plant height and stand. Data analysis were performed by geostatistical methods; the spatial dependence was established using experimental univariate and cross semivariograms with datasets. Modeling semivariograms led to the generation of attribute maps by Kriging, Cokriging and Collocated cokriging. The estimation by Cokriging and Collocated cokriging was similar from Kriging. From the semivariogram, it was possible to identify that soil and plant attributes were spatially related with each other. The soya growth was mainly changed by slope of the area and little changed by saturated hydraulic conductivity.
ABSTRACT
OBJECTIVES: To determine the effect of interpolation functions when processing the digital periapical images. MATERIALS AND METHODS: The digital images were obtained by Digora and CDR system on the dry skull and human subject. 3 oral radiologists evaluated the 3 portions of each processed image using 7 interpolation methods and ROC curves were obtained by trapezoidal methods. RESULTS: The heighest Az value(0.96) was obtained with cubic spline method and the lowest Az value(0.03) was obtained with facet model method in Digora system. The heighest Az value(0.79) was obtained with gray segment expansion method and the lowest Az value(0.07) was obtained with facet model method in CDR system. There was significant difference of Az value in original image between Digora and CDR system at alpha=0.05 level. There were significant differences of Az values between Digora and CDR images with cubic spline method, facet model method, linear interpolation method and non-linear interpolation method at alpha= 0.1 level.
Subject(s)
Humans , Radiography, Dental, Digital , ROC Curve , Skull , ToothABSTRACT
Image resampling is of particular interest in digital radiology. When resampling an image to a new set of coordinate, there appears blocking artifacts and image changes. To enhance image quality, interpolation algorithms have been used. Resampling is used to increase the number of points in an image to improve its appearance for display. The process of interpolation is fitting a continuous function to the discrete points in the digital image. The purpose of this study was to determine the effects of the seven interpolation functions when image resampling in digital periapical images. The images were obtained by Digora, CDR and scanning of Ektaspeed plus periapical radiograms on the dry skull and human subject. The subjects were exposed to intraoral X-ray machine at 60kVp and 70 kVp with exposure time varying between 0.01 and 0.50 second. To determine which interpolation method would provide the better image, seven functions were compared ; (1) nearest neighbor (2) linear (3) non-linear (4) facet model (5) cubic convolution (6) cubic spline (7) gray segment expansion. And resampled images were compared in terms of SNR(Signal to Noise Ratio) and MTF(Modulation Transfer Function) coefficient value. The obtained results were as follows ; 1. The highest SNR value(75.96dB) was obtained with cubic convolution method and the lowest SNR value(72.44dB) was obtained with facet model method among seven interpolation methods. 2. There were significant differences of SNR values among CDR, Digora and film scan(p0.05). 4. There were significant differences of MTF coefficient values between linear interpolation method and the other six interpolation methods(p<0.05). 5. The speed of computation time was the fastest with nearest neighbor method and the slowest with non-linear method. 6. The better image was obtained with cubic convolution, cubic spline and gray segment method in ROC analysis. 7. The better sharpness of edge was obtained with gray segment expansion method among seven interpolation methods.