Efficient photometric feature extraction in a hierarchical learning scheme for nodule detection
by Ömer Muhammet Soysal; Jianhua Chen; Helmut Schneider
International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS), Vol. 2, No. 4, 2012

Abstract: In this paper, we present an efficient way of computing a run-length matrix which is used in extracting photometric features for computer vision applications such as object recognition and image retrieval. Our algorithm has two main steps which are quantisation and a fast indexing procedure for grey levels and their runs. We tested the features computed from the run-length matrix in our computer aided detection (CAD) system for lung nodule detection from computed tomography (CT) images. Our CAD system embeds a hierarchical learning scheme that allows multi-perspective and multi-level object recognition. The classification results obtained using the run-length features are promising.

Online publication date: Sun, 21-Oct-2012

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com