Application of image processing techniques to aid in the detection of vertical root fractures in digital periapical radiography.

OBJECTIVES: To present an image processing framework to improve the detection of vertical root fractures (VRFs) in digital periapical radiography. MATERIALS AND METHODS: Thirty endodontically treated human teeth (15 of them fractured with a metal post inserted into them, and 15 for the control) were enclosed in a dry mandible and radiographed individually. The proposed framework was applied to the raw data, as a preprocessing step, and was composed of four stages: geometric adjustment and negative, denoising, adaptive contrast enhancement, and gamma correction. The contrast-to-noise ratio (CNR) and sharpness of the image's VRF region were used for the objective evaluation of the method. In addition, five examiners evaluated the original and enhanced images, using a 5-point scale to assess confidence. RESULTS: The objective results showed that the proposed framework increased the CNR of the VRF region by 173% compared to the standard preprocessing method provided by the detector's manufacturer. The results found by the human observers indicated that the area under the curve (AUC) and sensitivity of the diagnosis of VRF significantly increased by 4% and 17% (p ≤ 0.05), respectively, when the examiners evaluated the image with the proposed method concomitantly with the image available in the commercial software. However, the specificity was reduced. CONCLUSIONS: The proposed image processing framework can be used as an additional tool to that provided by the manufacturer to increase the sensitivity and AUC of the diagnosis of VRF. CLINICAL RELEVANCE: The proposed method can be easily used in clinical practice to aid VRF detection, since it does not incur high computational costs and does not increase the radiation dose applied to the patient.

A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography.

Architectural distortion (AD) is the earliest sign of breast cancer that can be detected on a mammogram, and it is usually associated with malignant tumors. Breast cancer is one of the major causes of death among women, and the chance of cure can increase significantly when detected early. Computer-aided detection (CAD) systems have been used in clinical practice to assist radiologists with the task of detecting breast lesions. However, due to the complexity and subtlety of AD, its detection is still a challenge, even with the assistance of CAD. Recently, the fusion of descriptors has become a trend for improving the performance of computer vision algorithms. In this work, we evaluated some local texture descriptors and their possible combinations, considering different fusion approaches, for application in CAD systems to improve AD detection. In addition, we present a novel fusion-based texture descriptor, the Completed Mean Local Mapped Pattern (CMLMP), which is based on complementary information between three LMP operators (signal, magnitude and center) and the local differences between pixel values and the mean value of a neighborhood. We compared the performance of the proposed descriptor with two other well-known descriptors: the Completed Local Binary Pattern (CLBP) and the Completed Local Mapped Pattern (CLMP), for the task of detecting AD in 350 digital mammography clinical images. The results showed that the descriptor proposed in this work outperforms the others, for both individual and fused approaches. Moreover, the choice of the fusion operator is crucial because it results in different detection performances.

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.