高精細液晶ディスプレイの視覚的評価 : 胸部腫瘤陰影像に対する診断において

Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model

In this paper, we employed neural network ensemble for FPs reduction in detecting lung nodules in chest radiographs. In our scheme, the ensemble consists of four modified forward neural networks, each one of them was trained with the back propagation algorithm to distinct a different type of non-nodules from nodules. The outputs of all the individual neural networks were combined by a modified forward mixing ANN. The performance of our scheme for false positive reduction was evaluated by use of FROC. With neural network ensemble, the false positive rate of CAD scheme1 was reduced for 44% (from 2.86 to 1.6 positives per image), at an overall sensitivity of 60%. We also compared our scheme with other researches. The result demonstrates the superiority of it over other ones. We believe that the proposed method is useful in false positives reduction in the diagnosis of lung nodules in chest radiograph.

We have developed a novel method called local contralateral subtraction for reduction of false positives reported by a computer-aided diagnosis (CAD) scheme for detection of lung nodules in chest radiographs. Our method is based on the removal of normal structures in the regions of interest (ROIs), based on symmetry between the left and right lungs. In our method, two ROIs were extracted, one from the position where a candidate of a nodule is located, and the other from the anatomically corresponding location in the opposite lung, which contains similar normal structures. We employed a wavelet-based multiresolution image registration method to match the two ROIs, and subtraction was performed. A signal- to-noise ratio (SNR) between a central region and the adjacent background region was calculated for quantification of the remaining structures in the subtracted ROI. The SNR was then used for distinction between nodules and false positives. In an analysis of 550 ROIs consisting of 51 nodules and 499 false positives reported as detected nodules by our current CAD scheme, we were able to eliminate 44% of false positives with loss of only one nodule with this new method. This result indicates that our new method is effective in reducing false positives due to normal anatomic structures, and thus can improve the performance of our CAD scheme for detection of pulmonary nodules in chest radiographs.

To improve the detection of nodules in chest radiographs, large databases of chest radiographs with annotated, proven nodules are needed for training of both radiologists and computer-aided detection systems. The construction of such databases is a laborious and time-consuming task. This study presents a novel technique to produce large amounts of chest x-rays with annotated, simulated nodules. Realistic nodules in radiographs are generated using real nodules segmented from CT images. Results from an observer study indicate that the simulated nodules can not be distinguished from real nodules. This method has great potential to aid the development of automated detection systems and to generate teaching files for human observers.

Observer studies are frequently performed to test new modalities. Correct study design is important to generate reliable results. Two most frequently used observer study designs are the sequential and the independent reading design. We investigated the effect of different observer study designs on reader performance results and statistical power. The study included multiple assessments of chest radiographs (CXR) with bone suppression images (BSI) for the detection of lung nodules. In a fully crossed study design 8 observers assessed first radiographs without and with BSI sequentially. Secondly they scored radiographs independently having BSI available from the beginning. Five months later, the same readers scored the same cases again in an independent reading session, completing the three scorings for CXRs with BSI. Observer performance was compared using multi reader multi case (MRMC) receiver operating characteristics (ROC). To estimate reader variance, Dorfman, Berbaum, Metz (DBM) variance component estimates were calculated. No significant difference between the sequential and the independent reading sessions could be found (p=0.51; p=0.61). Both reading designs showed increased performance with BSI, with a significant increase for the sequential and the independent reading session after five months (p=0.002; p=0.007). Total observer variance between sequential and independent reading design remained the same. A strong increase of uncorrelated components was found in the independent reading sessions, masking the ability to demonstrate differences in observer performance across modalities. In conclusion, results of the sequential and the independent study design did not show a significant difference. The independent study design had less power compared to the sequential study design due to a strong increase of uncorrelated variance components.

We examined the usefulness of an automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules in chest radiographs, based on ROC analysis of radiologists’ performance. The average performance (Az = 0.817) of all radiologists with the computer output was significantly improved compared to that (Az = 0.743) without the computer output (p < 0.01). However, the performance (Az = 0.889) of the computer result was much better than these results. The effect of the computer output on radiologists’ decision-making was clearly demonstrated by the high correlation coefficient (R = 0.845) between the average change (%) in radiologists’ confidence level from without to with the computer output and the likelihood measures of malignancy (%). In other words, radiologists tended to change their confidence level to be consistent with the likelihood measure of malignancy presented. This effect tended to be greater in a group of attending radiologists (R = 0.872) compared with a group of residents (R = 0.714). These results indicated that this automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules has a high potential to improve the diagnostic accuracy of radiologists’ decision-making when the radiologist uses the computer output as a “second opinion.”

Detectability of lung nodules using flat panel detector with dual energy subtraction by two shot method: Evaluation by ROC method

A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database

Computer-vision techniques based on wavelets are presented with applications to computer-aided diagnosis (CAD) in medical images. CAD is a method for assisting radiologists’ interpretation of medical images. CAD schemes act as a second reader and alert radiologists to suspicious lesions. Development of CAD schemes is an active area of research, motivated by the fact that radiologists tend to misdiagnose cancerous lesions in medical images. In the past several years, we have been extensively developing wavelet-based techniques for CAD schemes. Development of these techniques has led to a new CAD scheme and has yielded significant improvements in the performance of the existing CAD schemes. This chapter describes two wavelet-based, low-level computer vision techniques: simultaneous segmentation and registration, and matching pursuit with optimally weighted wavelet packets. We present their application to the computer-aided detection of lung nodules in chest radiographs and microcalcifications in mammograms.

To determine the value of digital storage-phosphor radiography (SR) on the detection and identification of subtle lung nodules, postero-anterior (PA) and lateral (LAT) film-screen (FR) chest radiographs were compared with isodose SR images of 45 patients with metastatic malignancies. The SR postprocessing was done with a particular mode previously optimized for routine chest radiography. Pulmonary metastases were found in 34 patients and were proved or excluded by CT (n = 28) or longterm follow-up FR (n = 17). Chest images were divided into four regions for evaluation of image quality, number of lung nodules per region and marked pulmonary structures by receiver-operating characteristics (ROC) analysis (45 patients; 125 nodules; 2810 observations; five readers). Of the nodules selected for an ROC study 82 % were 0.5–1.0 cm in diameter. Overall image quality was rated better for FR concerning lung fields (PA) and mediastinum/hilum (LAT). More lung-field nodules were detected on FR than on SR chest images (P < 0.05). Use of FR was superior to SR in the general identification of nodules (PA chest), especially concerning intermediate (P < 0.01) and subtle abnormalities (P < 0.05), whereas there was no significant difference for LAT chest images. Our results show, that currently FR still has advantages over SR in the detection and identification of subtle lung nodules in routine clinical radiography.

BackgroundChest radiographs are frequently used to evaluate pediatric patients with COVID-19 infection during the current pandemic. Despite the minimal radiation dose associated with chest radiography, children are far more sensitive to ionizing radiation's carcinogenic effects than adults. This study aimed to examine whether serum biochemical markers could be potentially used as a surrogate for imaging findings to reduce radiation exposure.MethodsThe retrospective posthoc analysis of 187 pediatric patients who underwent initial chest radiographs and serum biochemical parameters on the first day of emergency department admission. The cohort was separated into two groups according to whether or not the initial chest radiograph revealed evidence of pneumonia. Spearman's rank correlation was used to connect serum biochemical markers with observations on chest radiographs. The Student's t-test was employed for normally distributed data, and for non-normally distributed data, the Mann–Whitney U test was used. A simple binary logistic regression was used to determine the importance of LDH in predicting chest radiographs. The discriminating ability of LDH in predicting chest radiographs was determined using receiver operating characteristics (ROC) analysis. The cut-off value was determined using Youden's test. Interobserver agreement was quantified using the Cohen k coefficient.Results187 chest radiographs from 187 individual pediatric patients (95 boys and 92 girls; mean age ± SD, 10.1 ± 6.0 years; range, nine months–18 years) were evaluated. The first group has 103 patients who did not have pneumonia on chest radiographs, while the second group contains 84 patients who had evidence of pneumonia on chest radiographs. GGO, GGO with consolidation, consolidation, and peri-bronchial thickening were deemed radiographic evidence of pneumonia in group 2 patients. Individuals in group 2 with radiological indications of pneumonia had significantly higher LDH levels (p = 0.001) than patients in group 1. The Spearman's rank correlation coefficient between LDH and chest radiography score is 0.425, showing a significant link. With a p-value of < 0.001, the simple binary logistic regression analysis result validated the relevance of LDH in predicting chest radiography. An abnormal chest radiograph was related to LDH > 200.50 U/L (AUC = 0.75), according to the ROC method. Interobserver agreement between the two reviewers was almost perfect for chest radiography results in both groups (k = 0.96, p = 0.001).ConclusionThis study results show that, compared to other biochemical indicators, LDH has an 80.6% sensitivity and a 62% specificity for predicting abnormal chest radiographs in a pediatric patient with confirmed COVID-19 infection. It also emphasizes that biochemical measures, rather than chest radiological imaging, can detect the pathogenic response to COVID-19 infection in the chest earlier. As a result, we hypothesized LDH levels might be potentially used instead of chest radiography in children with COVID-19, reducing radiation exposure.

X-ray computed tomography (CT) imaging plays an essential role in disease diagnosis due to its noninvasive, painless mode and superior penetration depth. However, the resolution of the soft tissue ...

Flat-panel displays (FPDs) will soon become ubiquitous products in our homes, workplaces, and a number of places in between. Familiar display applications pioneered by the venerable cathode-ray tube (CRT) will progressively utilize a number of different FPDs. Flat-panel displays will also initiate and pioneer new applications, as was the case with notebook computers and personal digital assistants.The future proliferation of FPDs can no longer be doubted for two simple reasons. First, such devices help us cope with this already information-saturated society. Second, the technological momentum and financial commitment needed for FPDs to take hold are already irreversibly set in place. It is the insatiable current and near-future demand that are pulling the field, and that are fueling a flurry of research and development activities. The display market has grown in size from tens of millions of dollars only a decade ago to a nearly ten-billion-dollar industry. This market growth is expected to continue at a brisk pace during the next decade.This of course is in no way bad news for materials scientists and engineers. We nearly always benefit from development of new and complex products. It is in regard to these rather incipient but advanced manufacturing technologies where we often encounter challenging, relevant, and interesting materials problems. The better we address these obstacles during these early and therefore critical times, the more successful the FPD technology will be in fulfilling the potential opportunities that lie ahead.

We retrospectively evaluated the usefulness of computer-aided diagnosis (CAD) schemes to radiologist performance in the simultaneous detection of vertebral fractures and lung nodules on chest radiographs. We evaluated posteroanterior and lateral chest images of 21 patients with vertebral fractures, 31 patients with lung nodules, and 10 persons acting as controls. The total number of subjects was 60 because both lesions were present in four patients. Eighteen radiologists were asked to detect vertebral fractures and nodules simultaneously on posteroanterior and lateral images. The radiologists indicated their confidence level ratings regarding the presence or absence of lesions and the most likely location of each lesion on either posteroanterior or lateral images, first without and then with CAD output. The observers' performance was evaluated with use of receiver operating characteristic (ROC) and jackknife free-response ROC curves. With the CAD scheme, the average area under the ROC curve for detection of vertebral fractures improved from 0.906 to 0.951 (p = 0.002). That for lung nodules also improved, but the improvement was not statistically significant (0.804-0.816, p = 0.297). The figure-of-merit values obtained with the jackknife free-response ROC program improved from 0.585 to 0.680 (p < 0.001) for vertebral fractures and from 0.622 to 0.650 (p = 0.017) for nodules, both results having statistical significance. Average sensitivity in the detection of lesions improved from 59.8% to 69.3% for vertebral fractures and from 64.9% to 67.6% for nodules. In the detection of vertebral fractures and lung nodules on chest images, diagnostic accuracy among radiologists improves with the use of CAD.

According to World Health Organization, pneumonia is the respiratory disease with the highest pediatric mortality rate accounting for 15% of all deaths of children under 5 years old worldwide. The diagnosis of pneumonia is commonly made by clinical criteria with support from ancillary studies and also laboratory findings. Chest imaging is commonly done with chest X-rays and occasionally with a chest CT scan. Lung ultrasound is a promising alternative for chest imaging; however, interpretation is subjective and requires adequate training. In the present work, a two-class classification algorithm based on four Gray-level co-occurrence matrix texture features (i.e., Contrast, Correlation, Energy and Homogeneity) extracted from lung ultrasound images from children aged between six months and five years is presented. Ultrasound data was collected using a L14-5/38 linear transducer. The data consisted of 22 positive- and 68 negative-diagnosed B-mode cine-loops selected by a medical expert and captured in the facilities of the Instituto Nacional de Salud del Nino (Lima, Peru), for a total number of 90 videos obtained from twelve children diagnosed with pneumonia. The classification capacity of each feature was explored independently and the optimal threshold was selected by a receiver operator characteristic (ROC) curve analysis. In addition, a principal component analysis was performed to evaluate the combined performance of all the features. Contrast and correlation resulted the two more significant features. The classification performance of these two features by principal components was evaluated. The results revealed 82% sensitivity, 76% specificity, 78% accuracy and 0.85 area under the ROC.