Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives

• Published in: Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Vol. 230; no. 1; p. 58
• Main Authors: Krishnamurthy, Senthilkumar, Narasimhan, Ganesh, Rengasamy, Umamaheswari
• Format: Journal Article
• Language: English
• Published: England 01.01.2016
• Subjects: Algorithms; Analysis of Variance; Humans; Imaging, Three-Dimensional - methods; Lung - diagnostic imaging; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Radiographic Image Interpretation, Computer-Assisted - methods; Tomography, X-Ray Computed - methods; lung cancer; shape feature extraction; three-dimensional segmentation; juxta-pleural nodule; Computed tomography; morphology processing
• ISSN: 2041-3033
• DOI: 10.1177/0954411915619951

The three-dimensional analysis on lung computed tomography scan was carried out in this study to detect the malignant lung nodules. An automatic three-dimensional segmentation algorithm proposed here efficiently segmented the tissue clusters (nodules) inside the lung. However, an automatic morphological region-grow segmentation algorithm that was implemented to segment the well-circumscribed nodules present inside the lung did not segment the juxta-pleural nodule present on the inner surface of wall of the lung. A novel edge bridge and fill technique is proposed in this article to segment the juxta-pleural and pleural-tail nodules accurately. The centroid shift of each candidate nodule was computed. The nodules with more centroid shift in the consecutive slices were eliminated since malignant nodule's resultant position did not usually deviate. The three-dimensional shape variation and edge sharp analyses were performed to reduce the false positives and to classify the malignant nodules. The change in area and equivalent diameter was more for malignant nodules in the consecutive slices and the malignant nodules showed a sharp edge. Segmentation was followed by three-dimensional centroid, shape and edge analysis which was carried out on a lung computed tomography database of 20 patient with 25 malignant nodules. The algorithms proposed in this article precisely detected 22 malignant nodules and failed to detect 3 with a sensitivity of 88%. Furthermore, this algorithm correctly eliminated 216 tissue clusters that were initially segmented as nodules; however, 41 non-malignant tissue clusters were detected as malignant nodules. Therefore, the false positive of this algorithm was 2.05 per patient.