Pulmonary micronodule detection from 3D chest CT

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

Research output: Contribution to journalConference article

26 Citations (Scopus)

Abstract

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 5mm in size using a series of 3D niters. 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.

Original languageEnglish (US)
Pages (from-to)821-828
Number of pages8
JournalLecture Notes in Computer Science
Volume3217
Issue number1 PART 2
StatePublished - Dec 1 2004
EventMedical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France
Duration: Sep 26 2004Sep 29 2004

Fingerprint

Nodule
Computed Tomography
Tomography
Medical imaging
Vessel
Filter
Lung
False Positive
Lung Cancer
Medical Imaging
Modality
Elimination
High Resolution

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chang, S., Emoto, H., Metaxas, D. N., & Axel, L. (2004). Pulmonary micronodule detection from 3D chest CT. Lecture Notes in Computer Science, 3217(1 PART 2), 821-828.
Chang, Sukmoon ; Emoto, Hirosh ; Metaxas, Dimitris N. ; Axel, Leon. / Pulmonary micronodule detection from 3D chest CT. In: Lecture Notes in Computer Science. 2004 ; Vol. 3217, No. 1 PART 2. pp. 821-828.
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Chang, S, Emoto, H, Metaxas, DN & Axel, L 2004, 'Pulmonary micronodule detection from 3D chest CT', Lecture Notes in Computer Science, vol. 3217, no. 1 PART 2, pp. 821-828.

Pulmonary micronodule detection from 3D chest CT. / Chang, Sukmoon; Emoto, Hirosh; Metaxas, Dimitris N.; Axel, Leon.

In: Lecture Notes in Computer Science, Vol. 3217, No. 1 PART 2, 01.12.2004, p. 821-828.

Research output: Contribution to journalConference article

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N2 - 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 5mm in size using a series of 3D niters. 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.

AB - 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 5mm in size using a series of 3D niters. 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.

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Chang S, Emoto H, Metaxas DN, Axel L. Pulmonary micronodule detection from 3D chest CT. Lecture Notes in Computer Science. 2004 Dec 1;3217(1 PART 2):821-828.