| Title | An integrated segmentation and shape based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT. |
| Publication Type | Journal Article |
| Year of Publication | 2017 |
| Authors | Alilou, M, Beig N, Orooji M, Rajiah P, Velcheti V, Rakshit S, Reddy N, Yang M, Jacono F, Gilkeson RC, Linden P, Madabhushi A |
| Journal | Medical physics |
| Date Published | 2017 Mar 14 |
| ISSN | 2473-4209 |
| Abstract | Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans. While there has been substantial interest in the use of textural analysis for radiomic characterization of lung nodules, relatively less work has been done in shape based characterization of lung nodules, particularly with respect to granulmoas and adenocarcinomas. The primary goal of this study is to evaluate the role of 3D shape features for discrimination of benign granulomas from malignant adenocarcinomas on lung CT images. Towards this end we present an integrated framework for segmentation, feature characterization and classification of these nodules on CT. |
| DOI | 10.1002/mp.12208 |
| PDF Link | |
| Alternate Journal | Med Phys |
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