Classifying pulmonary nodules using dynamic enhanced CT images based on CT number histogram

Kazuhiro Minami; Yoshiki Kawata; Noboru Niki; Hironobu Ohmatsu; Kiyoshi Mori; Kouzou Yamada; Kenji Eguchi; Masahiro Kaneko; Noriyuki Moriyama

doi:10.1117/12.770198

17 March 2008 Classifying pulmonary nodules using dynamic enhanced CT images based on CT number histogram

Kazuhiro Minami, Yoshiki Kawata, Noboru Niki, Hironobu Ohmatsu, Kiyoshi Mori, Kouzou Yamada, Kenji Eguchi, Masahiro Kaneko, Noriyuki Moriyama

Author Affiliations +

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 69152P (2008) https://doi.org/10.1117/12.770198
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Pulmonary nodule evaluation based on analyses of contrast-enhanced CT images becomes useful for differentiating malignant and benign nodules. There are several types of nodule regarding inside density (such as solid, mixed GGO, and pure GGO) and size. This paper presents relationships between contrast enhancement characteristics and nodule types. Thin-section, contrast-enhanced CT (pre-contrast, and post-contrast series acquired at 2 and 4 minutes) was performed on 86 patients with pulmonary nodules (42 benign and 44 malignant). Nodule regions were segmented from an isotropic volume reconstructed from each image series. In this study, the contrast-enhancement characteristics of nodules were quantified by using CT number histogram. The CT number histograms inside the segmented nodules were computed on pre-contrast and post-contrast series. A feature characterizing variation between two histograms was computed by subtracting the histogram of post-contrast series from that of pre-contrast series, and dividing the summation of subtracted frequency of each bin by the volume of the segmented nodule on pre-contrast series. The nodules were classified into five types (α, β, γ, δ, and ε) on the basis of internal features extracted from CT number histogram on pre-contrast series. The nodule data set was categorized into subset through the nodule type and size and the performance of the feature to classify malignant from benign nodules was evaluated for each subset.

Citation Download Citation

Kazuhiro Minami, Yoshiki Kawata, Noboru Niki, Hironobu Ohmatsu, Kiyoshi Mori, Kouzou Yamada, Kenji Eguchi, Masahiro Kaneko, and Noriyuki Moriyama "Classifying pulmonary nodules using dynamic enhanced CT images based on CT number histogram", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152P (17 March 2008); https://doi.org/10.1117/12.770198

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Computed tomography

Image segmentation

Lithium

Cancer

3D image processing

Feature extraction

Solids

Show All Keywords

Keywords/Phrases

Search In:

Publication Years