Design, Evaluation and Implementation of a Pipeline for Semi-Automatic Lung Nodule Segmentation
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Lung cancer is the most common type of cancer in the world and always manifests as lung nodules. Nodules are small tumors that consist of lung tissue. They are usually spherical in shape and their cores can be either solid or subsolid. Nodules are common in lungs, but not all of them are malignant. To determine if a nodule is malignant or benign, attributes like nodule size and volume growth are commonly used. The procedure to obtain these attributes is time consuming, and therefore calls for tools to simplify the process.
The purpose of this thesis work was to investigate the feasibility of a semi-automatic lungnodule segmentation pipeline including volume estimation. This was done by implementing, tuning and evaluating image processing algorithms with different characteristics to create pipeline candidates. These candidates were compared using a similarity index between their segmentation results and ground truth markings to determine the most promising one.
The best performing pipeline consisted of a fixed region of interest together with a level set segmentation algorithm. Its segmentation accuracy was not consistent for all nodules evaluated, but the pipeline showed great potential when dynamically adapting its parameters for each nodule. The use of dynamic parameters was only brie y explored, and further research would be necessary to determine its feasibility.
Place, publisher, year, edition, pages
2016. , 37 p.
IdentifiersURN: urn:nbn:se:liu:diva-126075ISRN: LiTH-ISY-EX--16/4925--SEOAI: oai:DiVA.org:liu-126075DiVA: diva2:911649
Sectra Imaging IT AB
Subject / course
Computer Vision Laboratory