A multiclassifier approach for lung nodule classification

• Type: Article in International Scientific Journal
• Date: 2006

Authors

Carlos S Pereira (Author) Other; Luis A Alexandre (Author) Other; Ana Maria Mendonca (Author) FEUP; Aurelio Campilho (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: IMAGE ANALYSIS AND RECOGNITION, PT 2

Vol. 13

Pages: 612-623

Indexing

ISI Proceedings

ISI Web of Knowledge - 8 Citations

ISI Web of Science

Scopus - 11 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Technological sciences > Engineering > Biomedical enginnering

Other information

Authenticus ID: P-004-PXP

DOI: 10.1007/11867661_55

Abstract (EN): The aim of this paper is to examine a multiclassifier approach to the classification of the lung nodules in X-ray chest radiographs. The approach investigated here is based on an image region-based classification whose output is the information of the presence or absence of a nodule in an image region. The classification was made, essentially, in two steps: firstly, a set of rotation invariant features was extracted from the responses of a multi-scale and multi-orientation filter bank; secondly, different classifiers (multi-layer perceptrons) are designed using different features sets and trained in different data. These classifiers are further combined in order to improve the classification performance. The obtained results are promising and can be used for reducing the false-positives nodules detected in a computer-aided diagnosis system.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12