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: Fri, 29-Aug-2014

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