Anatomic region-based dynamic range compression for chest radiographs using warping transformation of correlated distribution

The purpose of this research is to investigate the effectiveness of our novel dynamic range compression for chest radiographs. Dynamic range compression preserves detail information, making diagnosis easier when using narrow dynamic range viewing systems such as monitors. First, an automated segmentation method was used to detect the lung region. The combined region of mediastinum, heart and subdiaphragm was defmed based on the lung region. The coffelated distributions, between a pixel value and its neighboring averaged pixel value, for the lung region and the combined region were calculated. According to the appearance of overlapping of two distributions, the warping function was decided. After pixel values were warped, the pixel value range of the lung region was compressed while preserving the detail information. The perfonnance was evaluated with our criterion function which was the contrast divided by the moment. For seventy-one screening chest images from Johns Hopkins University Hospital, this method improved our criterion function at 1 1 .7% on average. The warping transformation algorithm based on the correlated distribution was effective in compressing the dynamic range while simultaneously preserving the detail information. Key Words: Dynamic Range Compression, Chest Radiograph, Anatomic Information, Lung Region, Image Processing, Correlated Distribution, Warping Transformation