Abstract
Detection and segmentation of fissures is useful in the clinical interpretation of CT lung images to diagnose the presence of pathologies in the human lungs. A new automated method based on marker-based watershed transformation has been proposed to segment the fissures considering its unique structure as a long connected component. Marker based watershed transformation is applied and morphological operations are employed to specify the internal and external markers. The smaller regions in the resulting image are removed by a novel procedure called Small Segment Removal Algorithm (SSRA) to segment the fissures alone. The performance of the method is validated by experimenting with 6 CT image sets. An expert radiologist observation is used as reference to assess the performance. A promising accuracy of 96.61% is shown with the rms error in the range of 0.877±0.224 mm for the left oblique fissure and 0.803±0.262 mm for the right oblique fissure.
Highlights
The lobar fissures are low contrast surfaces with worldwide
Anatomical parts and detection of abnormalities present in Automation of fissure segmentation is essential to the lungs, is necessary to enable the doctors to assist the doctors in identifying the fissures in the entire detect the various chronic obstructive pulmonary diseases stack of CT scan slices
The method does not depend on the prior anatomical knowledge of the individual subjects
Summary
The lobar fissures are low contrast surfaces with worldwide. Computed Tomography is the mostly adopted blurred boundaries when viewed on cross sectional CT imaging technique to diagnose and detect the pathologies images (Zhang et al, 2006). Anatomical parts and detection of abnormalities present in Automation of fissure segmentation is essential to the lungs, is necessary to enable the doctors to assist the doctors in identifying the fissures in the entire detect the various chronic obstructive pulmonary diseases stack of CT scan slices. Fissures are the separating boundaries between the of fissures are of clinical importance in the assessment of lung disease on a lobar level. The right oblique or major fissure divides the middle pulmonary diseases across the lobar areas A robust detection of pulmonary fissure could provide a basis for accurate lobe segmentation that may help to facilitate preoperative planning and postoperative assessment in the clinical practice. Automation of fissure detection is a challenging task due to its low contrast, fuzzy appearance in CT images. The surrounding structures like airways, vascular branches, nodules and noise artifacts present make the detection process more difficult
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