A new method for false-positive reduction in detection of lung nodules in CT images

This paper proposes a novel approach for false-positive reduction in lung nodule detection based on structure relationship analysis between nodule candidate and vessel, and the modified surface normal overlap descriptor. On one hand, a large number of false nodules attached to vessels can be removed by analyzing the relationship between nodule candidates and their attached tissues. On the other hand, Low-contrast nonsolid nodules are discriminated from the candidates with modified surface normal overlap descriptor. The proposed method has been trained and validated on a clinical dataset of 90 thoracic CT scans using a low dose levels that contain 90 nodules (62 solid nodules, 25 ground-glass opacity nodules and 3 mixed nodules) determined by a ground truth reading process.

[1]  L. Schwartz,et al.  Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm , 2003, Journal of applied clinical medical physics.

[2]  S. Armato,et al.  Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. , 2003, Medical physics.

[3]  Anselmo Cardoso de Paiva,et al.  Methodology for automatic detection of lung nodules in computerized tomography images , 2010, Comput. Methods Programs Biomed..

[4]  Berkman Sahiner,et al.  Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. , 2002, Medical physics.

[5]  Hiroshi Fujita,et al.  Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique , 2001, IEEE Transactions on Medical Imaging.

[6]  Jan Cornelis,et al.  A novel computer-aided lung nodule detection system for CT images. , 2011, Medical physics.

[7]  Milan Sonka,et al.  A New Method for Spherical Object Detection and Its Application to Computer Aided Detection of Pulmonary Nodules in CT Images , 2007, MICCAI.

[8]  Joyoni Dey,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[9]  Piergiorgio Cerello,et al.  A novel multithreshold method for nodule detection in lung CT. , 2009, Medical physics.

[10]  Georgy Gimel'farb,et al.  3D shape analysis for early diagnosis of malignant lung nodules. , 2011, Information processing in medical imaging : proceedings of the ... conference.

[11]  Qiang Li,et al.  Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. , 2003, Medical physics.

[12]  K. Doi,et al.  Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. , 2008, Academic Radiology.

[13]  Abbas Z. Kouzani,et al.  Automated detection of lung nodules in computed tomography images: a review , 2010, Machine Vision and Applications.

[14]  Kenji Suzuki,et al.  Maximal partial AUC feature selection in computer-aided detection of hepatocellular carcinoma in contrast-enhanced hepatic CT , 2012, Medical Imaging.

[15]  Anthony P. Reeves,et al.  A multiscale Laplacian of Gaussian filtering approach to automated pulmonary nodule detection from whole-lung low-dose CT scans , 2009, Medical Imaging.