Digitizing radiographic films: a simple way to evaluate indirect digital images

OBJECTIVES: This study applied a simple method to evaluate the performance of three digital devices (two scanners and one digital camera) using the reproducibility of pixel values attributed to the same radiographic image. METHODS: Using the same capture parameters, a radiographic image was repeatedly digitized in order to determine the variability of pixel values given to the image throughout the digitization process. One coefficient value was obtained and was called pixel value reproducibility. RESULTS: A significant difference in pixel values was observed among the three devices for the digitized images (ANOVA, p<0.00001). There was significant pixel value variability at the same digitization conditions for one scanner and the digital camera. CONCLUSIONS: Digital devices may assign pixel values differently in consecutive digitization depending on the optical density of the radiographic image and the equipment. The pixel value reproducibility was not satisfactory as tested for two devices. It is maybe advisable knowing the digitization variations regarding pixel values whenever using digital radiography images in longitudinal clinical examinations.

Digitizing radiographic films: a simple way to evaluate indirect digital images.

OBJECTIVES: This study applied a simple method to evaluate the performance of three digital devices (two scanners and one digital camera) using the reproducibility of pixel values attributed to the same radiographic image. METHODS: Using the same capture parameters, a radiographic image was repeatedly digitized in order to determine the variability of pixel values given to the image throughout the digitization process. One coefficient value was obtained and was called pixel value reproducibility. RESULTS: A significant difference in pixel values was observed among the three devices for the digitized images (ANOVA, p<0.00001). There was significant pixel value variability at the same digitization conditions for one scanner and the digital camera. CONCLUSIONS: Digital devices may assign pixel values differently in consecutive digitization depending on the optical density of the radiographic image and the equipment. The pixel value reproducibility was not satisfactory as tested for two devices. It is maybe advisable knowing the digitization variations regarding pixel values whenever using digital radiography images in longitudinal clinical examinations.

Evaluating noise in digitized radiographic images by means of histogram

To evaluate the performance of three digital devices regarding the noise added to digital radiographic images containing different optical densities. METHODS: A radiographic image was digitized repeatedly ten times using two scanners (HP 4c/T and HP 5370C) and a digital camera (Nikon 990). A histogram tool measured a mean pixel value and the standard deviation of the region of interest in each image. Both values were used to calculate the image noise at the different optical densities. RESULTS: The noise values found were different for all devices and optical densities. There was a statistically significant difference (p<0.05) between the scanner HP 4c/T and the digital camera regarding the noise values. There was a significant correlation (p<0.05) between the noise values found for the HP 4c/T scanner and the digital camera and between both scanners (p<0.01). CONCLUSION: The noise added to the image was higher for scanner HP 4c/T and less for the digital camera. The noise was higher at the lower optical densities for the scanners. It seems that depending on the equipment and the optical density, a variable amount of noise can be incorporated to the images.

OBJETIVOS: Avaliar três equipamentos digitais em relação ao ruído agregado as imagens radiográficas digitalizadas contendo diferentes densidades ópticas. MATERIAL AND MÉTODO: Uma imagem radiográfica foi digitalizada seqüencialmente dez vezes usando dois escaneres (HP 4c/T and HP 5370C) e uma câmera digital (Nikon 990). Por meio do histograma foram medidos os valores de pixels e os desvios-padrões da região de interesse de cada imagem. Ambos valores foram utilizados para o cálculo do ruído nas diferentes densidades ópticas. RESULTADOS: Os valores encontrados para o ruído foram diferentes para cada equipamento e para cada densidade óptica. Houve uma diferença estatística significante entre os valores de ruído encontrados para o escaner HP 4c/T e a câmera digital (p<0.05). Houve uma correlação significante entre os valores do ruído encontrados para o escaner HP 4c/T e a câmera digital (p<0.05) e entre os dois escaneres (p<0.01). CONCLUSÕES: O ruído agregado à imagem foi maior para o escaner HP 4c/T e menor para a câmera digital. O ruído foi maior nas densidades ópticas menores para os dois escaneres. Dependendo do equipamento e da densidade óptica uma quantidade variável de ruído pode ser agregado às imagens.

Evaluating noise in digitized radiographic images by means of histogram.

UNLABELLED: To evaluate the performance of three digital devices regarding the noise added to digital radiographic images containing different optical densities. METHODS: A radiographic image was digitized repeatedly ten times using two scanners (HP 4c/T and HP 5370C) and a digital camera (Nikon 990). A histogram tool measured a mean pixel value and the standard deviation of the region of interest in each image. Both values were used to calculate the image noise at the different optical densities. RESULTS: The noise values found were different for all devices and optical densities. There was a statistically significant difference (p<0.05) between the scanner HP 4c/T and the digital camera regarding the noise values. There was a significant correlation (p<0.05) between the noise values found for the HP 4c/T scanner and the digital camera and between both scanners (p<0.01). CONCLUSION: The noise added to the image was higher for scanner HP 4c/T and less for the digital camera. The noise was higher at the lower optical densities for the scanners. It seems that depending on the equipment and the optical density, a variable amount of noise can be incorporated to the images.

Computer technique for digital radiographic images correction based on the digitizer characteristic curve.

This article presents a computer correction technique for radiographic digital images based on digitization process inaccuracy in pixel gray levels relative to the respective image optical densities. The technique consists of determining the digitizer characteristic curve by digitizing a step-wedge radiographic image with known optical densities. Calibration is done by determining the mathematical function that automatically adjusts the pixel gray levels to the expected values according to the manufacturer. Two laser film digitizers were investigated for which discrepancies between measured and expected pixel values were reported. For these cases, the proposed algorithm automatically adjusted the pixel gray levels of the digitized images. Using this method, the digital images show more accurate values of gray levels compared to the original radiographic image. In addition, it allows the development of uniform images databases.