Biomedical subjects

Kuniharu Imai

Publications and source records attributed to Kuniharu Imai.

5 recordsLinked to original sources

PubMed2006

Information loss in visual assessments of medical images.

We applied information theory to quantify information losses in assessing contrast-detail (C-D) analysis. Images of a C-D phantom were acquired with a flat panel detector (FPD) and a computed radiography (CR) by changing surface entrance doses. Six phantom radiographs (FPD: five images; CR: one image) were prepared for visual evaluations. Thirteen radiographers and two radiologists participated in the observation test. Detectability was defined as the shortest length of the cylinders of which border the observers could recognize from the background, and was recorded using row number. Information content was defined as the entropy summation operatorp(i)log(1/p(i)) with detection probabilities p(i), which were calculated from distribution of detection rate of the ith column. Information loss, in unit of bits, was calculated as the difference between information obtained and information content when all the columns were detected. The information losses decreased with the increase in cylinder diameters and with the increase in surface entrance dose. Because the information loss varies depending on distribution of detection rate, this method of using the information theory was expected to be more sensitive in evaluating the C-D image quality than using the averaged values of detectability.

Contrast Sensitivity↗

PubMed2006

The application of Markov theory to contrast-detail analysis.

RATIONALE AND OBJECTIVES: To improve the accuracy of contrast-detail (C-D) analysis, we have devised a new evaluation method of the detection performance in the C-D analysis by using the Markov process model. This article describes this new evaluation method and the results of applying it to the illustrative examples. MATERIALS AND METHODS: A commercially available C-D phantom was employed as a test object, and 20 phantom radiographs with an average background density of 0.8 were prepared. Thirty-five observers interpreted all phantom radiographs independently of each other with the room lights off. We assume the Markov model in which the selection of the smallest visible contrast for each row of disks with the same diameter depends only on the contrast selected as the smallest visible one for the same row in the just-before reading experiment. By using this Markov model, the contrast detectability for each disk diameter was calculated, and a C-D diagram was constructed based on these results. The conventional C-D diagram was also derived from the averages of contrast detectability obtained from the interpretations of all phantom radiographs. RESULTS: All C-D diagrams obtained from our devised method agreed well with each other and were in good accord with the conventional C-D diagram from the interpretations of all phantom radiographs. CONCLUSIONS: We have devised a new evaluation method of C-D analysis by using the Markov model and have shown this method will be consistent with the conventional evaluation method in the C-D analysis.

Analysis of Variance↗

PubMed2005

A method of clustering observers with different visual characteristics.

Evaluation of observer's image perception in medical images is important, and yet has not been performed because it is difficult to quantify visual characteristics. In the present study, we investigated the observer's image perception by clustering a group of 20 observers. Images of a contrast-detail (C-D) phantom, which had cylinders of 10 rows and 10 columns with different diameters and lengths, were acquired with an X-ray screen-film system with fixed exposure conditions. A group of 10 films were prepared for visual evaluations. Sixteen radiological technicians, three radiologists and one medical physicist participated in the observation test. All observers read the phantom radiographs on a transillumination image viewer with room lights off. The detectability was defined as the shortest length of the cylinders of which border the observers could recognize from the background, and was recorded using the number of columns. The detectability was calculated as the average of 10 readings for each observer, and plotted for different phantom diameter. The unweighted pair-group method using arithmetic averages (UPGMA) was adopted for clustering. The observers were clustered into two groups: one group selected objects with a demarcation from the vicinity, and the other group searched for the objects with their eyes constrained. This study showed a usefulness of the cluster method to select personnels with the similar perceptual predisposition when a C-D phantom was used in image quality control.

Cluster Analysis↗

PubMed2004

[Grouping of observer's visual characteristics on the basis of difference in the criteria of visual detection and selection of the fittest clustering method:verification of observer's group with radiologist-like visual characteristics].

Various clustering methods are used in cluster analyses, with each clustering method demonstrating unique advantages. Therefore, it is important to make the best use of the advantages each method provides. We have recognized that it is necessary in the evaluation of X-ray images to classify observers quantitatively according to visual characteristics (grouping of observers) and have clustered observers using the UPGMA method, which is one of the clustering methods. We found that the observers were clustered into two different groups, one with radiologist-like characteristics and the other with medical physicist-like characteristics. Furthermore, we suggested that the group with radiologist-like characteristics was suitable for QC of X-ray images. However, it is doubtful whether the UPGMA method is most suitable for the grouping of observers. In this work we clustered observers using various clustering methods and examined the most suitable method for the evaluation of X-ray images. The results showed that the ward method was least suitable for the grouping of observers, and they were distinctly grouped into two different categories by using a further method.

Cluster Analysis↗

PubMed2003

[Classification of observer-groups with cluster analysis and the criteria of visual decision for each group].

It is important that the evaluation of X-ray images include the observer's visual characteristics. However, evaluations of X-ray images that include these characteristics are not performed because of the difficulty of quantitatively elucidating visual characteristics. In this study, we classified observers into groups (clusters) by the same criteria of visual decision, using cluster analysis (unweighted Pair-Group method using arithmetic averages), and evaluated X-ray images on the basis of this separation. Clinical application is also discussed. It was found that observer clustering caused a decrease in between-observer variation. Observers were grouped into two different categories: one with the characteristics of radiologists and the other with the characteristics of medical physicists. Our results indicated that the group with the characteristics of radiologists was suitable for the quality control (QC) of X-ray images.

Cluster Analysis↗