Automatic Lung Segmentation Using Control Feedback System: morphology and Texture Paradigm

• Main Authors: M. Noor, Norliza, Than, Joel C. M., Rijal, Omar M., M. Kassim, Rosminah, Yunus, Ashari, Zeki, Amir A., Anzidei, Michele, Saba, Luca, Suri, Jasjit S.
• Format: Article
• Published: Springer New York LLC 2015
• Subjects: T Technology (General)
• Online Access: http://eprints.utm.my/id/eprint/57934/; http://dx.doi.org/10.1007/s10916-015-0214-6

Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung’s performance of segmentation was 96.52 % for Jaccard Index and 98.21 % for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), -1.15 % for Relative Area Error and 4.09 % Area Overlap Error. The right lung’s performance of segmentation was 97.24 % for Jaccard Index, 98.58 % for Dice Similarity, 0.61 mm for PDM, -0.03 % for Relative Area Error and 3.53 % for Area Overlap Error. The segmentation overall has an overall similarity of 98.4 %. The segmentation proposed is an accurate and fully automated system.