Skip Navigation

IEICE Transactions on Information and Systems 2005 E88-D(6):1273-1283; doi:10.1093/ietisy/e88-d.6.1273
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (1)
Right arrow Request Permissions
Google Scholar
Right arrow Articles by FUKANO, G.
Right arrow Articles by IINUMA, T.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Copyright © 2005 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Papers -- Biological Engineering

Eigen Image Recognition of Pulmonary Nodules from Thoracic CT Images by Use of Subspace Method

Gentaro FUKANO1,2, Yoshihiko NAKAMURA1, Hotaka TAKIZAWA1, Shinji MIZUNO1, Shinji YAMAMOTO1, Kunio DOI3, Shigehiko KATSURAGAWA4, Tohru MATSUMOTO5, Yukio TATENO5 and Takeshi IINUMA5

1 The authors are with Toyohashi University of Technology, Toyohashi-shi, 441–8580 Japan. E-mail: Gentarou.Fukano{at}jp.yokogawa.com, 2 The author is with Yokogawa Electric Corporation, Musashi-no-shi, 180–8750 Japan., 3 The author is with the University of Chicago, Chicago, IL, 60637, USA., 4 The author is with Kumamoto University, Kumamoto-shi, 860–8555 Japan., 5 The authors are with National Institute of Radiological Sciences, Chiba-shi, 263–8555 Japan.

We have proposed a recognition method for pulmonary nodules based on experimentally selected feature values (such as contrast, circularity, etc.) of pathologic candidate regions detected by our Variable N-Quoit (VNQ) filter. In this paper, we propose a new recognition method for pulmonary nodules by use of not experimentally selected feature values, but each CT value itself in a region of interest (ROI) as a feature value. The proposed method has 2 phases: learning and recognition. In the learning phase, first, the pathologic candidate regions are classified into several clusters based on a principal component score. This score is calculated from a set of CT values in the ROI that are regarded as a feature vector, and then eigen vectors and eigen values are calculated for each cluster by application of principal component analysis to the cluster. The eigen vectors (we call them "eigen-images") corresponding to the S-th largest eigen values are utilized as base vectors for subspaces of the clusters in a feature space. In the recognition phase, correlations are measured between the feature vector derived from testing data and the subspace which is spanned by the eigen-images. If the correlation with the nodule subspace is large, the pathologic candidate region is determined to be a nodule, otherwise, it is determined to be a normal organ. In the experiment, first, we decide on the optimal number of subspace dimensions. Then, we demonstrated the robustness of our algorithm by using simulated nodule images.

Key Words: pulmonary nodules, thoracic CT, computer aided diagnosis, automatic clustering, subspace method

Manuscript received July 28, 2004. Manuscript revised January 11, 2005.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.