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

Gentaro FUKANO
Yoshihiko NAKAMURA
Hotaka TAKIZAWA
Shinji MIZUNO
Shinji YAMAMOTO
Kunio DOI
Shigehiko KATSURAGAWA
Tohru MATSUMOTO
Yukio TATENO
Takeshi IINUMA

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E88-D    No.6    pp.1273-1283
Publication Date: 2005/06/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.6.1273
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Biological Engineering
Keyword: 
pulmonary nodules,  thoracic CT,  computer aided diagnosis,  automatic clustering,  subspace method,  

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Summary: 
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.


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