Open AccessEnhancement of radiographic images in patients with lung nodules
Author(s) -
Rajab Maher I.,
Eskandar Ayman A.
Publication year - 2011
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/j.1759-7714.2011.00045.x
Subject(s) - medicine , radiography , lung , radiology , chest radiograph , jpeg , dicom , artificial intelligence , computer vision , image (mathematics) , computer science
Detection of lung nodules in a chest Radiograph is very difficult due to sensitivity to noise, lighting, and similar disturbances of the blood vessels and trachea. Therefore, such images need to be carefully examined to identify and characterize lung lesions. However, human interpretations are usually contradictory and may cause confusion. Current works propose an image processing technique based on frequency domain processing to clarify X‐ray radiographic images taken in patients with a variety of lung lesions. The Picture Archiving and Communication Systems workstation allows transferring radiographic data from DICOM into JPEG image formats. In the preprocessing phase, the lung nodules are identified by an experienced chest radiologist and used for extracting regions of interest. Subsequently, low‐pass followed by emphasis high‐pass frequency filters are applied to enhance the images with appropriate cut‐off frequencies. It has been found that high‐frequency domain image filtering enhances the morphological features of lung masses. Enhanced images are then visually arbitrated by an expert radiologist. We found that the characteristics of lung lesions are easily identified after this process.