[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.

[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.