[Development of computerized method for automated classification of the body parts in digital radiographs].

Nippon Hoshasen Gijutsu Gakkai zasshi

PubMedID: 12740561

Kawashita I, Ishida T, Arimura H, Katsuragawa S, Komi M, Awai K, Hori S, Doi K. [Development of computerized method for automated classification of the body parts in digital radiographs]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2003;59(3):396-7.
PURPOSE
In picture archiving and communication systems (PACS), the information on the body parts included in radiographs is often not or incorrectly recorded in an image header. In order to apply the computer-aided diagnosis (CAD) system in the PACS environment, the body parts in radiographs need to be recognized correctly by computer. The purpose of this study is to develop a computerized method for correctly classifying the body parts in digital radiographs based on a template matching technique.

METHODS/MATERIALS
The image database used in this study was 1032 digital radiographs (14 x 17 inches) obtained with a computed radiography, and included 505 chest of postetroanterior view, 39 chest of lateral view, 241 abdomen, 108 pelvis, 10 upper limbs, 125 lower limbs, and 4 thoracic spine. In this method, test images were classified into four body parts, i.e., (1) chest, (2) abdomen, (3) pelvis, and (4) upper/lower limbs and thoracic spine. This computerized method was tested with 852 images, since 180 images were employed for creation of 98 templates, which represented the average radiographs for various body parts. Our approach was to examine the similarity of a given test image with templates by use of the cross-correlation values as the similarity measures. The body part of the test image was identified as the body part in the template yielding the maximum correlation value. Our method consisted of the following five steps. First, test images were classified into one of three groups; i.e. 1) chest and abdomen, 2) pelvis, and 3) upper/lower limbs and thoracic spine by using the templates obtained from images with the average size and position. Second, the remaining uncertain images were classified by using additional templates in various directions. Third, the chest and abdomen group was separated into two subgroups; i.e.chest and abdomen. Fourth, in order to classify some uncertain images, templates were shifted horizontally and vertically. Fifth, outer pixels of templates were eliminated to avoid the misclassification due to x-ray collimation.

RESULTS
Our preliminary results indicated that the body parts for 850 cases (99.8%) were correctly classified with our method.

CONCLUSIONS
This method would be useful for automated identification of the body parts in radiographs when various CAD systems would be implemented in the PACS environment.