چکیده (انگلیسی):

In automated pulmonary nodules extraction and lung disease
diagnosis by image processing techniques, image segmentation is
utilized as a primary and the most essential step of lung tumor analysis.
But due to extensive similarity between pulmonary vessels, bronchus
and arteries in lung region and the low contrast of the Computed-
Tomography (CT) image the accuracy of lung tumor diagnosis is highly
dependent on the precision of segmentation. Therefore, precise lung CT
image segmentation has become a challenging preprocessing task for
every lung disease pathological application.In this study, a novel Four-
Directional Thresholding (FDT) technique is introduced. This
propounded technique segments the pulmonary parenchyma in
Computed-Tomography (CT) images using the Similarity-Based
Segmentation (SBS). The proposed technique aims to augment the
precision of the CT image thresholding by implementing an advanced
thresholding approach from four different directions in which the
determination of pixels’ value as being either on foreground or
background is highly dependent on its adjacent pixel’s intensity value
and the final decision is made based on all four directions’
thresholding results. In this study the importance of neighbor pixels in
precision of thresholding with FDT technique is demonstrated and the
effectiveness of FDT method has been evaluated on different CT
images. Eventually the result of segmentation using FDT method is
compared by other precursors techniques, which corroborates the high
exactitude of proposed technique.