A CAD tool for chest radiographs consists of several procedures and the very first step is segmentation of lung fields. We
develop a novel methodology for segmentation of lung fields in chest radiographs that can satisfy the following two
requirements. First, we aim to develop a segmentation method that does not need a training stage with manual estimation
of anatomical features in a large training dataset of images. Secondly, for the ease of implementation, it is desirable to
apply a well established model that is widely used for various image-partitioning practices. The Chan-Vese active
contour model, which is based on Mumford-Shah functional in the level set framework, is applied for segmentation of
lung fields. With the use of this model, segmentation of lung fields can be carried out without detailed prior knowledge
on the radiographic anatomy of the chest, yet in some chest radiographs, the trachea regions are unfavorably segmented
out in addition to the lung field contours. To eliminate artifacts from the trachea, we locate the upper end of the trachea,
find a vertical center line of the trachea and delineate it, and then brighten the trachea region to make it less distinctive.
The segmentation process is finalized by subsequent morphological operations. We randomly select 30 images from the
Japanese Society of Radiological Technology image database to test the proposed methodology and the results are shown.
We hope our segmentation technique can help to promote of CAD tools, especially for emerging chest radiographic
imaging techniques such as dual energy radiography and chest tomosynthesis.
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