Automatic Detection Of Central-chest Lymph Nodes In 3d Mdct scans

Open Access
Zambad, Piyush Sunilkumar
Graduate Program:
Electrical Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
March 27, 2015
Committee Members:
  • William Evan Higgins, Thesis Advisor
  • Vishal Monga, Thesis Advisor
  • Kultegin Aydin, Thesis Advisor
  • Lymph-nodes
  • automatic
  • detection
  • lung-cancer
  • Hessian
  • Harris
  • Region Growing
  • Mediastinal
The central-chest lymph-node biopsy is a major diagnostic procedure that greatly influences the efficacy of lung-cancer staging. Finding the accurate locations of signifi cant lymph nodes is a vital step in the diagnosis of lung cancer. In this thesis, we aim to develop a time-efficient and robust method for automatically detecting central-chest lymph-nodes in 3D MDCT chest images. The method employed here models lymph-nodes as 3D-ellipsoidal blob structures. The lymph-nodes are detected by a Harris-based Hessian analysis on the mediastinum region by removing the presegmented structures of the central-chest region followed by a customized region-growing and false-positive reduction. The method achieves a true detection rate of 57% and 61% with an average of 47 and 48 false-positives per case for lymph-nodes with short-axis length >= 7 mm and >= 10 mm respectively, evaluated over a dataset of 17 cases. The average computation time for the method is 31 minutes.