Abstrato

Lung nodule volume growth analysis and visualization through auto-cluster k-means segmentation and centroid/shape variance based false nodule elimination

Senthil Kumar TK, Ganesh EN, Umamaheswari R

The objective of this work was to develop an automated computerized algorithm to detect the cancerous nature of lung. An Auto Cluster K-means segmentation (ACKMS) was used to segment the lung nodules from CT scans. ACKMS algorithm was developed such that it selects initial clusters automatically by average minimum-maximum pixel computation on each row and column of CT image. All the candidate nodules segmented from the consecutive slices of CT scan were reconstructed to develop a 3D-image. The vessels and calcifications were eliminated by centroid and shape/edge variation analysis. Nodule growth analysis was carried out on real nodules remained after eliminating vessels and calcifications. The rate of nodule growth (RNG) was computed in terms of 3D-volume change. The CT scans of 34 patients taken at different time intervals were analyzed. In total, 400 to 600 candidate nodules of size>3 mm were segmented from every scan series. Out of the 34 shortlisted real nodules, 3 nodules had RNG value>1, confirming their malignant nature. For another 14 nodules, the RGN value was ranged in between 0.2 and 0.8, suggesting a need for another follow-up scan to confirm malignancy. The remaining 17 nodules showed RGN<0.2; seven nodules showed almost zero and in the remaining 10 cases follow-up scan was advised based on patients living habits and environmental conditions.