Curse of dimensionality often hinders the process of data mining. The data collected and analyzed generally contains huge number of dimensions or attributes and it may be the case that not all of the attributes are necessary for the data mining task to be performed on the data. Traditionally data dimensionality reduction techniques like Principal Component Analysis or Linear Discriminant analysis have been used to address this problem. But, these methods move the original data to a transformed space. However, the need might be to remain in the original attribute space and identify the key attributes for data analysis. This need has given rise to the research area of feature subset selection. In this paper we have used solid angle measure to tackle the problem of dimension reduction in OCT retinal data. Optical Coherence Tomography (OCT) is a frequently used and established medical imaging technique. It is widely used, among other application, to obtain high-resolution images of the retina and the anterior segment of the eye. Solid angle measure is used to characterize and select features obtained from OCT retinal images. The application of solid angle in feature selection, as proposed in this paper, is a unique approach to OCT image data mining. The experimental results with real life datasets presented in this paper will demonstrate the effectiveness of the proposed method.

Date Published: 28 May 2013
PDF: 10 pages
Proc. SPIE 8755, Mobile Multimedia/Image Processing, Security, and Applications 2013, 87550G (28 May 2013); doi: 10.1117/12.2014393

Published in SPIE Proceedings Vol. 8755:
Mobile Multimedia/Image Processing, Security, and Applications 2013
Sos S. Agaian; Sabah A. Jassim; Eliza Yingzi Du, Editor(s)