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Junji Shiraishi

Publications and source records attributed to Junji Shiraishi.

22 records · Page 2Linked to original sources

Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience.

PURPOSE: To evaluate radiologists' performance for determining a distinction between benign and malignant pulmonary nodules on chest radiographs without and with use of a computer-aided diagnosis scheme. MATERIALS AND METHODS: Fifty-three chest radiographs that depicted 31 primary lung cancers and 22 benign nodules were used. The likelihood measure of malignancy for each nodule was determined by using an automated computerized scheme. Sixteen radiologists (nine attending radiologists and seven radiology residents) participated in an observer study in which cases were interpreted first without and then with use of the scheme. The radiologists' performance was evaluated with receiver operating characteristic analysis. RESULTS: The mean area under the best-fit binormal receiver operating characteristic curve plotted in the unit square (Az) values of radiologists who interpreted images without and with the scheme were 0.743 and 0.817, respectively. The performance of radiologists was improved significantly when the scheme was used (P =.002). However, the performance (Az = 0.889) of the computer alone exceeded these results by a substantial margin. The average change in radiologists' confidence level for interpretation without and with the scheme was highly correlated (r = 0.845) with the likelihood measure of malignancy, which was presented as computer output. CONCLUSION: This scheme for computer-aided diagnosis has the potential to improve the accuracy of radiologists' performance in the classification of benign and malignant solitary pulmonary nodules.

Diagnosis, Computer-Assisted↗

[Development of an image processing scheme for chest radiographs using a dot printer].

The performance of dot printers has recently been improved. Images output by dot printers can provide simple, economical medical reference images if important diagnostic information is not lost. We developed an image processing scheme for chest radiographs that employed a dot printer. We used two types of pixel value conversions, a nonlinear pixel value correction using a lookup table and a linear pixel value conversion using histogram analysis. The density distribution of chest radiographs was analyzed and classified into high-density and low-density images. The two types of pixel value conversions were used depending on the density of chest radiographs. Converted pixel value had density characteristics that were adapted to the output image of the dot printer, and thus the density distributions of the lungs of radiographs became comparable. In addition, an adaptive unsharp masking technique with processing parameters optimized for each of the lungs and mediastinum was applied. An ROC study for the detection of lung nodules was carried out to evaluate the performance of dot printer images. The area under the ROC curve (A(z)) for dot printer images was 0.816, while sensitivity and specificity were 77.6% and 75.2%, respectively. The performance indicated the usefulness of our image processing scheme.

Humans↗

Computer-aided diagnosis in chest radiography: results of large-scale observer tests at the 1996-2001 RSNA scientific assemblies.

Since 1996, computer-aided diagnosis (CAD) schemes have been presented as interactive demonstrations on computer workstations at each scientific assembly of the Radiological Society of North America. The schemes involved (a) detection of pulmonary nodules, (b) temporal subtraction, (c) detection of interstitial lung disease, (d) differential diagnosis of interstitial lung disease, and (e) distinction between benign and malignant pulmonary nodules on chest radiographs. Large-scale observer tests were carried out to examine how radiologists can benefit from CAD systems. Observer performance was evaluated by analysis of receiver operating characteristic (ROC) curves. The statistical significance of the difference between the areas under the ROC curves without and with CAD was analyzed with the Student t test. In all of the tests, the diagnostic accuracy of the radiologists in total improved significantly when CAD was used. This result provides additional evidence that CAD has the potential to improve the performance of radiologists in their decision-making process in interpreting chest radiographs.

Diagnosis, Computer-Assisted↗