Multistage segmentation model and SVM-ensemble for precise lung nodule detection

International Journal of Computer Assisted Radiology and Surgery
Syed Muhammad NaqiMussarat Yasmin

Abstract

Lung cancer detection at its initial stages increases the survival chances of patients. Automatic detection of lung nodules facilitates radiologists during the diagnosis. However, there is a challenge of false positives in automated systems which may lead to wrong findings. Precise segmentation facilitates to accurately extract nodules from lung CT images in order to improve performance of the diagnostic method. A multistage segmentation model is presented in this study. The lung region is extracted by applying corner-seeded region growing combined with differential evolution-based optimal thresholding. In addition to this, morphological operations are applied in boundary smoothing, hole filling and juxtavascular nodule extraction. Geometric properties along with 3D edge information are applied to extract nodule candidates. Geometric texture features descriptor (GTFD) followed by support vector machine-based ensemble classification is employed to distinguish actual nodules from the candidate set. A publicly available dataset, namely lung image database consortium and image database resource initiative, is used to evaluate performance of the proposed method. The classification is performed over GTFD feature vector and the result...Continue Reading

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Citations

Jun 28, 2019·Microscopy Research and Technique·Tanzila Saba
Nov 11, 2019·Journal of Medical Systems·Tanzila SabaMuhammad Sharif
Jun 20, 2020·Journal of Digital Imaging·R Jenkin SujiW Wilfred Godfrey
Mar 23, 2019·Scientific Reports·Sajid Ali KhanKhalid Iqbal
Jan 8, 2019·Microscopy Research and Technique·Shahzad AkbarMahyar Kolivand
Oct 15, 2021·Interactive Cardiovascular and Thoracic Surgery·Hao-Jen WangChung-Ming Chen

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