Work

Reduction of false positives at vessel bifurcations in computerized detection of lung nodules

Year: 2014

Type: article

Abstract: Objective: We describe a new false positive (FP) reduction method based on surface features in our computerized detection system for lung nodules and evaluate the method using clinical chest computed tomography (CT) scans. Methods: In our detection method, nodule candidates are extracted using volumetric curvature-based thresholding and region growing. For various sizes of nodules, we adopt multiscale integration based on Hessian eigenvalues. For each nodule candidate, two surface features are calculated to differentiate nodules and FPs at vessel bifurcations. These features are fed into a quadratic classifier based on the Mahalanobis distance ratio. Results: In an experimental study involving 16 chest CT scans, the average number of FPs was reduced from 107.5 to 14.1 per case at 90% sensitivity. Conclusions: This proposed FP reduction method is effective in removing FPs at vessel bifurcations.

Source: Journal of Biomedical Graphics and Computing

DOI: 10.5430/jbgc.v4n3p36

Authors: Yukihiro Nomura, Mitsutaka Nemoto, Yoshitaka Masutani, Shouhei Hanaoka, Takeharu Yoshikawa, Soichiro Miki, Eriko Maeda, Naoto Hayashi, Naoki Yoshioka, Kuni Ohtomo

Institutions: University of Tokyo Hospital, Hiroshima City University, Sanno Hospital, Sanno Hospital

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W2139872012

FWCI: 1.491

Cites: 31

Cited by: 12

Related to: 10