Edge enhancement algorithm for low-dose X-ray fluoroscopic imaging

A blurring correction method suitable to analyze quantitative x-ray images derived from energy-resolving photon counting detector

Objective.The purpose of this study is to propose a novel blurring correction method that enables accurate quantitative analysis of the object edge when using energy-resolving photon counting detectors (ERPCDs). Although the ERPCDs have the ability to generate various quantitative analysis techniques, such as the derivations of effective atomic number (Zeff) and bone mineral density values, at the object edge in these quantitative images, accurate quantitative information cannot be obtained. This is because image blurring prevents the gathering of accurate primary x-ray attenuation information.Approach.We developed the following procedure for blurring correction. A 5 × 5 pixels masking region was set as the processing area, and the pixels affected by blurring were extracted from the analysis of pixel value distribution. The blurred pixel values were then corrected to the proper values estimated by analyzing minimum and/or maximum values in the set mask area. The suitability of our correction method was verified by a simulation study and an experiment using a prototype ERPCD.Main results. WhenZeffimage of aluminum objects (Zeff= 13) were analyzed without applying our correction method, regardless of raw data or correction data applying a conventional edge enhancement method, the properZeffvalues could not be derived for the object edge. In contrast, when applying our correction method, 82% of pixels affected by blurring were corrected and the properZeffvalues were calculated for those pixels. As a result of investigating the applicability limits of our method through simulation, it was proven that it works effectively for objects with 4 × 4 pixels or more.Significance. Our method is effective in correcting image blurring when the quantitative image is calculated based on multiple images. It will become an in-demand technology for putting a quantitative diagnosis into actual medical examinations.

A Fast Automatic Reconstruction Method for Panoramic Images Based on Cone Beam Computed Tomography

Panoramic images have been widely used in the diagnosis of dental diseases. In the process of panoramic image reconstruction, the position of the dental arch curve usually affects the quality of display content, especially the completion level of the panoramic image. In addition, the metal implants in the patient’s mouth often lead the contrast of the panoramic image to decrease. This paper describes a method to automatically synthesize panoramic images from dental cone beam computed tomography (CBCT) data. The proposed method has two essential features: the first feature is that the method can detect the dental arch curve through axial maximum intensity projection images over different ranges, and the second feature is that our method is able to adjust the intensity distribution of the implant in critical areas, to reduce the impact of the implant on the contrast of the panoramic image. The proposed method was tested on 50 CBCT datasets; the panoramic images generated by this method were compared with images attained from three other commonly used approaches and then subjectively scored by three experienced dentists. In the comprehensive image contrast score, the method in this paper has the highest score of 11.16 ± 2.64 points. The results show that the panoramic images generated by this method have better image contrast.

A review of airport dual energy X-ray baggage inspection techniques: Image enhancement and noise reduction

In this paper, we present a review of the research literature regarding applying X-ray imaging of baggage scrutiny at airport. It discusses multiple X-ray imaging inspection systems used in airports for detecting dangerous objects inside the baggage. Moreover, it also explains the dual energy X-ray image fusion and image enhancement factors. Different types of noises in digital images and noise models are explained in length. Diagrammatical representations for different noise models are presented and illustrated to clearly show the effect of Poisson and Impulse noise on intensity values. Overall, this review discusses in detail of Poisson and Impulse noise, as well as its causes and effect on the X-ray images, which create un-certainty for the X-ray inspection imaging system while discriminating objects and for the screeners as well. The review then focuses on image processing techniques used by different research studies for X-ray image enhancement, de-noising, and their limitations. Furthermore, the most related approaches for noise reduction and its drawbacks are presented. The methods that may be useful to overcome the drawbacks are also discussed in subsequent sections of this paper. In summary, this review paper highlights the key theories and technical methods used for X-ray image enhancement and de-noising effect on X-ray images generated by the airport baggage inspection system.

Real-time algorithm for Poissonian noise reduction in low-dose fluoroscopy: performance evaluation

BACKGROUND

Quantum noise intrinsically limits the quality of fluoroscopic images. The lower is the X-ray dose the higher is the noise. Fluoroscopy video processing can enhance image quality and allows further patient's dose lowering. This study aims to assess the performances achieved by a Noise Variance Conditioned Average (NVCA) spatio-temporal filter for real-time denoising of fluoroscopic sequences. The filter is specifically designed for quantum noise suppression and edge preservation. It is an average filter that excludes neighborhood pixel values exceeding noise statistic limits, by means of a threshold which depends on the local noise standard deviation, to preserve the image spatial resolution. The performances were evaluated in terms of contrast-to-noise-ratio (CNR) increment, image blurring (full width of the half maximum of the line spread function) and computational time. The NVCA filter performances were compared to those achieved by simple moving average filters and the state-of-the-art video denoising block matching-4D (VBM4D) algorithm. The influence of the NVCA filter size and threshold on the final image quality was evaluated too.

RESULTS

For NVCA filter mask size of 5 × 5 × 5 pixels (the third dimension represents the temporal extent of the filter) and a threshold level equal to 2 times the local noise standard deviation, the NVCA filter achieved a 10% increase of the CNR with respect to the unfiltered sequence, while the VBM4D achieved a 14% increase. In the case of NVCA, the edge blurring did not depend on the speed of the moving objects; on the other hand, the spatial resolution worsened of about 2.2 times by doubling the objects speed with VBM4D. The NVCA mask size and the local noise-threshold level are critical for final image quality. The computational time of the NVCA filter was found to be just few percentages of that required for the VBM4D filter.

CONCLUSIONS

The NVCA filter obtained a better image quality compared to simple moving average filters, and a lower but comparable quality when compared with the VBM4D filter. The NVCA filter showed to preserve edge sharpness, in particular in the case of moving objects (performing even better than VBM4D). The simplicity of the NVCA filter and its low computational burden make this filter suitable for real-time video processing and its hardware implementation is ready to be included in future fluoroscopy devices, offering further lowering of patient's X-ray dose.

Automatic reconstruction method for high-contrast panoramic image from dental cone-beam CT data

BACKGROUND AND OBJECTIVE

Panoramic images reconstructed from dental cone beam CT (CBCT) data have been effectively used in dental clinics for disease diagnosis. Panoramic images generally have low contrast because excessive non-interest tissues participate in the reconstruction, which may affect the diagnosis. In this study, we developed a fully automatic reconstruction method to improve the global and detail contrast of panoramic images.

METHODS

The proposed method consists of dental arch thickness detection, image synthesis, and image enhancement. First, the dental arch thickness is detected from an axial maximum intensity projection (MIP) image generated from the axial slices containing the teeth to reduce non-interest tissues in panoramic image reconstruction. Then, a new synthesis algorithm is proposed at image synthesis stage to reduce the effect of non-interest tissues on image contrast. Finally, an image enhancement algorithm is applied to the synthesized image to improve the detail contrast of the final panoramic image.

RESULTS

A total of 129 real clinical dental CBCT data sets were used to test the proposed method. The panoramic images generated by three methods were subjectively scored by three experienced dentists who were blinded to the generated method. The evaluation of image contrast included the maxillary, mandible, teeth, and particular region (root canal, crown reconstruction, implants, and metal brackets). The overall image contrast score revealed that the proposed method scored the highest of 11.03 ± 2.46, followed by the ray sum and x-ray methods with corresponding scores of 6.4 ± 1.65 and 5.35 ± 1.56. The results of expert subjective scoring indicated that the image contrast of the panoramic image generated by the proposed method is higher than those of existing methods.

CONCLUSIONS

The proposed method provides a quick, effective and robust solution to improve the global and detail contrast of the panoramic image generated from dental CBCT data.

A study on fluoroscopic images in exposure reduction techniques - Focusing on the image quality of fluoroscopic images and exposure images

The quality of the present day fluoroscopic images is sufficiently high for use as exposure images depending on the environment where the fluoroscopic images are recorded. In some facilities which use fluoroscopic images as exposure images they are recorded with a radiological x‐ray diagnostic device equipped with a fluoroscopic storage function. There are, however, cases where fluoroscopic images cannot be used as exposure images because the quality of the fluoroscopic image cannot be assured in the environment where the fluoroscopic images are recorded. This poses problems when stored fluoroscopic images are used in place of exposure images without any clearly established standard. In the present study, we establish that stored fluoroscopic images can be used as exposure images by using gray values obtained from profile curves. This study finds that replacement of stored fluoroscopic images with exposure images requires 20.1 or higher gray scale value differences between the background and signal, using a 20 cm thick acrylic phantom (here an adult abdomen as representing the human body) as the specific geometry. This suggests the conclusion that the gray value can be considered a useful index when using stored fluoroscopic images as exposure images.

Dental radiography image enhancement for treatment evaluation through digital image processing

Background

Evaluation of dental treatment is performed by observing dental periapical radiography to obtain information of filling’s condition, pulp tissue, remain dentin thickness, periodontal ligament, and lamina dura. Nevertheless, the radiographic image used often has low quality due to the level of x-ray radiation made low purposely in order to prevent health problem and limited tools capability. This low quality of the radiographic image, for examples, low image contrast, less brightness, and noise existence cause periapical radiography evaluation hard to be performed. This study aims to improve dental radiographic image quality for assisting pulp capping treatment evaluation.

Material and Methods

The research methodology consists of three main stages, i.e. data collection, image enhancement method production, and result validation. Radiographic image data collection in The Dental Hospital UMY. Image enhancement method has been conducted by comparing several methods: contourlet transform (CT), wavelet transform, contrast stretching (CS), and contrast limited adaptive histogram equalization (CLAHE) to reduce noise, to optimize image contrast, and to enhance image brightness.

Results

The result of this study is according to mean square error (MSE) and peak signal to noise ratio (PSNR) statistics evaluation, it obtains that the highest scores of MSE and PSNR in row gained from CT method totaled 5.441453 and 40.53652, followed by CLAHE method with the scores are 10.66326 and 38.00736, CS method whose scores are 12.39881 and 39.18518, and the last is wavelet method with the scores are 15.41569 and 36.25343.

Conclusions

Nonetheless, MSE and PSNR scores are not enough merely to give a recommendation of any suitable methods for improving contrast, therefore, it needs another success parameter coming from the dentist.

Key words:Dental radiography, image enhancement, digital image processing.

Small lung nodules detection based on local variance analysis and probabilistic neural network

BACKGROUND AND OBJECTIVE

In medical examinations doctors use various techniques in order to provide to the patients an accurate analysis of their actual state of health. One of the commonly used methodologies is the x-ray screening. This examination very often help to diagnose some diseases of chest organs. The most frequent cause of wrong diagnosis lie in the radiologist's difficulty in interpreting the presence of lungs carcinoma in chest X-ray. In such circumstances, an automated approach could be highly advantageous as it provides important help in medical diagnosis.

METHODS

In this paper we propose a new classification method of the lung carcinomas. This method start with the localization and extraction of the lung nodules by computing, for each pixel of the original image, the local variance obtaining an output image (variance image) with the same size of the original image. In the variance image we find the local maxima and then by using the locations of these maxima in the original image we found the contours of the possible nodules in lung tissues. However after this segmentation stage we find many false nodules. Therefore to discriminate the true ones we use a probabilistic neural network as classifier.

RESULTS

The performance of our approach is 92% of correct classifications, while the sensitivity is 95% and the specificity is 89.7%. The misclassification errors are due to the fact that network confuses false nodules with the true ones (6%) and true nodules with the false ones (2%).

CONCLUSIONS

Several researchers have proposed automated algorithms to detect and classify pulmonary nodules but these methods fail to detect low-contrast nodules and have a high computational complexity, in contrast our method is relatively simple but at the same time provides good results and can detect low-contrast nodules. Furthermore, in this paper is presented a new algorithm for training the PNN neural networks that allows to obtain PNNs with many fewer neurons compared to the neural networks obtained by using the training algorithms present in the literature. So considerably lowering the computational burden of the trained network and at same time keeping the same performances.