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Pulmonary Micronodule Detection from 3D Chest CT

2004; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-540-30136-3_100

1611-3349

Sukmoon Chang, Hirosh Emoto, Dimitris Metaxas, Leon Axel,

AI in cancer detection

Computed Tomography (CT) is one of the most sensitive medical imaging modalities for detecting pulmonary nodules. Its high contrast resolution allows the detection of small nodules and thus lung cancer at a very early stage. In this paper, we propose a method for automating nodule detection from high-resolution chest CT images. Our method focuses on the detection of discrete types of granulomatous nodules less than 5 mm in size using a series of 3D filters. Pulmonary nodules can be anywhere inside the lung, e.g., on lung walls, near vessels, or they may even be penetrated by vessels. For this reason, we first develop a new cylinder filter to suppress vessels and noise. Although nodules usually have higher intensity values than surrounding regions, many malignant nodules are of low contrast. In order not to ignore low contrast nodules, we develop a spherical filter to further enhance nodule intensity values, which is a novel 3D extension of Variable N-Quoit filter. As with most automatic nodule detection methods, our method generates false positive nodules. To address this, we also develop a filter for false positive elimination. Finally, we present promising results of applying our method to various clinical chest CT datasets with over 90% detection rate.