Feature subset selection using ICA for classifying emphysema in HRCT images

Mithun Nagendra Prasad, Arcot Sowmya, Inge Koch

Research output: Chapter in Book/Conference paperConference paper

11 Citations (Scopus)

Abstract

Feature subset selection, applied as a pre-processing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier performance. In recent years, data in some applications has increased in both the number of instances and features. It is in this context that we introduce a novel approach to reduce both instance and feature space through Independent Component Analysis (ICA) for the classification of Emphysema in High Resolution Computer Tomography (HRCT) images. The technique was tested successfully on 60 HRCT scans having Emphysema using three different classifiers (Naïve Bayes, C4.5 and Seeded K Means). The results were also compared against "density mask", a standard approach used for Emphysema detection in medical image analysis. In addition, the results were visually validated by radiologists.

Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
EditorsJ. Kittler, M. Petrou, M. Nixon
Pages515-518
Number of pages4
Volume4
DOIs
Publication statusPublished - 20 Dec 2004
Externally publishedYes
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: 23 Aug 200426 Aug 2004

Conference

ConferenceProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
CountryUnited Kingdom
CityCambridge
Period23/08/0426/08/04

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