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A Fully Automated Adaptive Unsharp Masking Technique in Digital Chest Radiograph

1992; Lippincott Williams & Wilkins; Volume: 27; Issue: 1 Linguagem: Inglês

10.1097/00004424-199201000-00014

1536-0210

Katsumi Abe, Shigehiko Katsuragawa, Yasuo Sasaki, Toru Yanagisawa,

Radiomics and Machine Learning in Medical Imaging

The authors are developing a fully automated adaptive unsharp masking technique with parameters that depend on regional image features of a digital chest radiograph. A chest radiograph includes various regions such as lung fields, retrocardiac area, and spine. These areas have very different texture patterns and optical densities. Therefore, for best evaluation, it is necessary to enhance the image contrast of each region by an optimal parameter. In the current study, a chest radiograph was automatically divided into three segments (lung field, retrocardiac area, and spine) by using a histogram analysis of pixel values. The lung fields and retrocardiac area were selectively enhanced with a small mask size and mild weighting factors that had been previously determined to be optimal. The spine was enhanced with a large mask size and adequate weighting factors. An observer performance test indicated that this technique provides excellent diagnostic accuracy for simulated nodules in chest radiographs.