Objectively measuring signal detectability, contrast, blur and noise in medical images using channelized joint observers

ABSTRACT To improve imaging systems and image processing techniques, objective image quality assessment is essential. Model observers adopting a task-based quality assessment strategy by estimating signal detectability measures, have shown to be quite successful to this end. At the same time, costly and time-consuming human observer experiments can be avoided. However, optimizing images in terms of signal detectability alone, still allows a lot of freedom in terms of the imaging parameters. More specifically, fixing the signal detectability defines a manifold in the imaging parameter space on which different “possible” solutions reside. In this article, we present measures that can be used to distinguish these possible solutions from each other, in terms of image quality factors such as signal blur, noise and signal contrast. Our approach is based on an extended channelized joint observer (CJO) that simultaneously estimates the signal amplitude, scale and detectability. As an application, we use this technique to design k-space trajectories for MRI acquisition. Our technique allows to compare the different spiral trajectories in terms of blur, noise and contrast, even when the signal detectability is estimated to be equal

Trabecular Bone Segmentation Based On Segment Profile Characteristics Using Extreme Learning Machine On Dental Panoramic Radiographs

Dental panoramic radiograph contains a lot of Information which one of them can be identified from trabecular bone structure. This research proposes segmentation of trabecular bone area on dental panoramic radiograph based on segment profile characteristics using Extreme Learning Machine as classification method. The input of this method is dental panoramic radiograph. The selection of region of interest (ROI) is performed on the lower jawbone of the trabecular bone area in which there are teeth and cortical bone. The ROI is subdivided into two where the upper ROI contains the teeth and the lower ROI contains cortical bone. After that, the result of the ROI deduction is done by preprocessing using mean and median filters for upper ROI and motion blur filter for lower ROI. The separate images are extracted each pixel into four features consisting of image intensity, 2D Gaussian filter with two different sigma, and Log Gabor filter for upper ROI. For lower ROI, five feature extractions are image intensity, Gaussian 2D filter with two different sigma, phase congruency, and Laplacian of Gaussian. Then used some sample pixels as training data to create Extreme Learning Machine model. The output of this classifier is the segmentation area of trabecular bone. On the upper ROI, the average of sensitivity, specificity, and accuracy were 82.31%, 93.67%, and 90.33%, respectively. While on the lower ROI obtained the average of sensitivity, specificity, and accuracy of 95.01%, 96.50%, and 95.29%, respectively

Effect of time lapse on the diagnostic accuracy of cone beam computed tomography for detection of vertical root fractures

Accurate and early diagnosis of vertical root fractures (VRFs) is imperative to prevent extensive bone loss and unnecessary endodontic and prosthodontic treatments. The aim of this study was to assess the effect of time lapse on the diagnostic accuracy of cone beam computed tomography (CBCT) for VRFs in endodontically treated dog’s teeth. Forty-eight incisors and premolars of three adult male dogs underwent root canal therapy. The teeth were assigned to two groups: VRFs were artificially induced in the first group (n=24) while the teeth in the second group remained intact (n=24). The CBCT scans were obtained by NewTom 3G unit immediately after inducing VRFs and after one, two, three, four, eight, 12 and 16 weeks. Three oral and maxillofacial radiologists blinded to the date of radiographs assessed the presence/absence of VRFs on CBCT scans. The sensitivity, specificity and accuracy values were calculated and data were analyzed using SPSS v.16 software and ANOVA. The total accuracy of detection of VRFs immediately after surgery, one, two, three, four, eight, 12 and 16 weeks was 67.3%, 68.7%, 66.6%, 64.6%, 64.5%, 69.4%, 68.7%, 68% respectively. The effect of time lapse on detection of VRFs was not significant (p>0.05). Overall sensitivity, specificity and accuracy of CBCT for detection of VRFs were 74.3%, 62.2%, 67.2% respectively. Cone beam computed tomography is a valuable tool for detection of VRFs. Time lapse (four months) had no effect on detection of VRFs on CBCT scans. © 2016, Associacao Brasileira de Divulgacao Cientifica. All rights reserved

Serial dependence in a simulated clinical visual search task.

In everyday life, we continuously search for and classify objects in the environment around us. This kind of visual search is extremely important when performed by radiologists in cancer image interpretation and officers in airport security screening. During these tasks, observers often examine large numbers of uncorrelated images (tumor x-rays, checkpoint x-rays, etc.) one after another. An underlying assumption of such tasks is that search and recognition are independent of our past experience. Here, we simulated a visual search task reminiscent of medical image search and found that shape classification performance was strongly impaired by recent visual experience, biasing classification errors 7% more towards the previous image content. This perceptual attraction exhibited the three main tuning characteristics of Continuity Fields: serial dependence extended over 12 seconds back in time (temporal tuning), it occurred only between similar tumor-like shapes (feature tuning), and only within a limited spatial region (spatial tuning). Taken together, these results demonstrate that serial dependence influences shape perception and occurs in visual search tasks. They also raise the possibility of a detrimental impact of serial dependence in clinical and practically relevant settings, such as medical image perception

Image presentation: Implications of Processing and Display

cdc:131061Presentation at the NIOSH Scientific Workshop : Application of the ILO International Classification of Radiographs of Pneumoconioses to Digital Chest Radiographic Images (2008 : Mar 12-13 : Washington, DC

Objective evaluation method using multiple image analyses for panoramic radiography improvement

Introduction: In the standardization of panoramic radiography quality, the education and training of beginners on panoramic radiographic imaging are important. We evaluated the relationship between positioning error factors and multiple image analysis results for reproducible panoramic radiography. Material and methods: Using a panoramic radiography system and a dental phantom, reference images were acquired on the Frankfurt plane along the horizontal direction, midsagittal plane along the left-right direction, and for the canine on the forward-backward plane. Images with positioning errors were acquired with 1-5 mm shifts along the forward-backward direction and 2-10 degrees rotations along the horizontal (chin tipped high/low) and vertical (left-right side tilt) directions on the Frankfurt plane. The cross-correlation coefficient and angle difference of the occlusion congruent plane profile between the reference and positioning error images, peak signal-to-noise ratio (PSNR), and deformation vector value by deformable image registration were compared and evaluated. Results: The cross-correlation coefficients of the occlusal plane profiles showed the greatest change in the chin tipped high images and became negatively correlated from 6 degrees image rotation (r = -0.29). The angle difference tended to shift substantially with increasing positioning error, with an angle difference of 8.9 degrees for the 10 degrees chin tipped low image. The PSNR was above 30 dB only for images with a 1-mm backward shift. The positioning error owing to the vertical rotation was the largest for the deformation vector value. Conclusions: Multiple image analyses allow to determine factors contributing to positioning errors in panoramic radiography and may enable error correction. This study based on phantom imaging can support the education of beginners regarding panoramic radiography

DETECTION OF CARIES ADJACENT TO TOOTH COLORED PROXIMAL RESTORATIONS USING STATIONARY INTRAORAL TOMOSYNTHESIS

Objectives: Caries adjacent to restorations (CAR) is the most common reason for replacing restorations. This study compared the ability of stationary intraoral tomosynthesis (s-IOT) and conventional bitewing radiographs in detecting CAR. Methods: Extracted teeth (N=77) with 113 proximal tooth-colored restorations were used. Observers (N=7) utilized a 5-point scale to rate their confidence that CAR was present and stereomicroscopy was used to establish ground truth. Results: S-IOT had a statistically higher (ANOVA p0.05). Conclusion: S-IOT showed higher diagnostic accuracy and sensitivity than conventional bitewing radiographs for detecting caries around proximal composite restorations. While the clinical effect size is small, s-IOT is a promising imaging modality for advancing the detection of CAR.Master of Scienc

Digital chest radiography: an update on modern technology, dose containment and control of image quality

The introduction of digital radiography not only has revolutionized communication between radiologists and clinicians, but also has improved image quality and allowed for further reduction of patient exposure. However, digital radiography also poses risks, such as unnoticed increases in patient dose and suboptimum image processing that may lead to suppression of diagnostic information. Advanced processing techniques, such as temporal subtraction, dual-energy subtraction and computer-aided detection (CAD) will play an increasing role in the future and are all targeted to decrease the influence of distracting anatomic background structures and to ease the detection of focal and subtle lesions. This review summarizes the most recent technical developments with regard to new detector techniques, options for dose reduction and optimized image processing. It explains the meaning of the exposure indicator or the dose reference level as tools for the radiologist to control the dose. It also provides an overview over the multitude of studies conducted in recent years to evaluate the options of these new developments to realize the principle of ALARA. The focus of the review is hereby on adult applications, the relationship between dose and image quality and the differences between the various detector systems