Image feature analysis and computer‐aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields

TLDR

The study aims to develop a computer‑vision algorithm to detect lung nodules in digital chest radiographs by analyzing nodule features and surrounding anatomy. The method uses a difference‑image approach to suppress background lung anatomy, followed by feature‑extraction tests for circularity, size, and threshold variation to isolate suspected nodules. Preliminary results show high true‑positive and low false‑positive rates for peripheral lung nodules, indicating the algorithm could aid clinicians and improve early lung cancer detection.

Abstract

We are investigating the characteristic features of lung nodules and the surrounding normal anatomic background in order to develop an algorithm of computer vision for use as an aid in the detection of nodules in digital chest radiographs. Our technique involves an attempt to eliminate the background anatomic structures in the lung fields by means of a difference image approach. Then, feature‐extraction techniques, such as tests for circularity, size, and their variation with threshold level, are applied so that suspected nodules can be isolated. Preliminary results of this automated detection scheme yielded high true‐positive rates and low false‐positive rates in the peripheral lung regions of the chest. This detection scheme, which can assist the final diagnosis by the clinician, has the potential to improve the early detection of lung carcinomas.