Feature extraction for classification of thin-layer chromatography images

• Type: Article in International Scientific Journal
• Date: 2005

Authors

Sousa, AV (Author) Other; Mendonca, AM (Author) FEUP; Campilho, A (Author) FEUP; Aguiar, R (Author) Other; Miranda, CS (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: IMAGE ANALYSIS AND RECOGNITION

Vol. 13

Pages: 974-981

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 5 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Technological sciences > Technology > Computer technology > Image processing ; Technological sciences > Engineering > Biomedical enginnering

Other information

Authenticus ID: P-000-5X6

DOI: 10.1007/11559573_118

Abstract (EN): Thin-Layer Chromatography images are used to detect and identify the presence of specific oligosaccharides, expressed by the existence, at different positions, of bands in the gel image. 1D gaussian deconvolution, commonly used for band detection, does not produce good results due to the large curvature observed in the bands. To overcome this uncertainty on the band position, we propose a novel feature extraction methodology that allows an accurate modeling of curved bands. The features are used to classify the data into two different classes, to differentiate normal from pathologic cases. The paper presents the developed methodology together with the analysis and discussion of the results.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8