



@book {2197,
	title = {Automatic lane detection in chromatography images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7325 LNCS},
	year = {2012},
	note = {Conference code: 91199Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-31298-4_22Language of Original Document: EnglishCorrespondence Address: Moreira, B.M.; Instituto de Engenharia Biom{\'e}dica, Universidade Do Porto, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: moreira@fe.up.ptReferences: Fried, B., Sherma, J., (1999) Thin-Layer Chromatography, , Marcel Dekker, New York;Rodrigues, L.G., Ferraz, M.J., Rodrigues, D., Pais-Vieira, M., Lima, D., Brady, R.O., S{\'a}-Miranda, M.C., Neurophysiological behavioral and morphological abnormalities in the Fabry knockout mice (2009) Neurobiology of Disease, 33 (1), pp. 48-56; 
Elstein, D., Altarescu, G., Beck, M., (2010) Fabry Disease, , Springer, Heidelberg; 
Houck, M.M., Siegel, J.A., (2010) Fundamentals of Forensic Science, , Elsevier, Oxford; 
Machado, A.M.C., Campos, M.F.M., Siqueira, A.M., De Carvalho, O.S.F., An iterative algorithm for segmenting lanes in gel electrophoresis images (1997) Proc. X Brazilian Symposium on Computer Graphics and Image Processing, pp. 140-146. , October; 
Elder, J.K., Southern, E.M., Computer-aided analysis of one dimensional restriction fragment gels (1987) Nucleic Acid and Protein Sequence Analysis - A Practical Aproach, pp. 165-172. , Bishop, M.J., Rawlings, C.J. (eds.) IRL Press, Oxford; 
Bajla, I., Holl{\"a}nder, I., Fluch, S., Burg, K., Koll{\'a}r, M., An alternative method for electrophoretic gel image analysis in the GelMaster software (2005) Computer Methods and Programs in Biomedicine, 77, pp. 209-231; 
Lin, C., Ching, Y., Yang, Y., An Automatic Method to Compare the Lanes in Gel Electrophoresis (GE) Images (2007) IEEE Transaction on Information Technology in Biomedicine, 11 (2), pp. 179-189; 
Akbari, A., Fritz, A., Jackobsen, K.S., Automatic lane detection and separation in one dimensional gel images using continuous wavelet transform (2010) The Royal Society of Chemistry, Analytical Methods, 2, pp. 1360-1371; 
Sousa, A.V., Aguiar, R.L., Mendon{\c c}a, A.M., Campilho, A.C., Automatic Lane and Band Detection in Images of Thin Layer Chromatography (2004) LNCS, 3212, pp. 158-165. , Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. Springer, Heidelberg; 
Sotaquira, M., On the use of distance maps in the analysis of 1D DNA gel images (2009) ICDIP 2009: Proceedings of the International Conference on Digital Image Processing, pp. 172-176. , IEEE Computer Society; 
Barrantes, P., Alvarado, P., Lane Detection on Gel Electrophoresis Images using Active Shape Models (2011) Proc. of the Conference on Technologies for Sustainable Development, TSD 2011, pp. 43-46. , February; 
Mendon{\c c}a, A.M., Sousa, A.V., S{\'a}-Miranda, M.C., Campilho, A.C., Automatic segmentation of chromatographic images for region of interest delineation (2011) Proc. SPIE, 7962, pp. 79623B1-79623B7; 
Chau, F., Liang, Y., Gao, J., Shao, X., (2004) Chemometrics - from Basics to Wavelet Transform, , John Wiley \& Sons, New Jersey; 
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T., (1992) Numerical Recipes in C: The Art of Scientific Computing, , Syndicate of the University of Cambridge; 
Soille, P., Jones, C.D., Smith, A.B., Roberts, E.F., (2004) Morphological Image Analysis - Principles and Applications, , Springer; 
Visa, S., Ralescu, A., Learning Imbalanced and Overlapping Classes using Fuzzy Sets Workshop on Learning from Imbalanced Datasets II, ICML (2003)
},
	month = {2012///},
	pages = {180 - 187},
	address = {Aveiro},
	abstract = {This paper proposes a method for automating the detection of lanes in Thin-Layer Chromatography images. Our approach includes a preprocessing step to detect the image region of interest, followed by background estimation and removal. This image is then projected onto the horizontal direction to integrate the information into a one-dimensional profile. A smoothing filter is applied to this profile and the outcome is the input of the lane detection process, which is performed in three phases. The first one aims at obtaining an initial set of candidate lanes that are further validated or removed in the second phase. The last phase is a refinement step that allows the inclusion of lanes that are not clearly distinguishable in the profile and that were not included in the initial set. The method was evaluated in 66 chromatography images and achieved values of recall, precision and F                        β -measure of 97.0\%, 99.4\% and 98.2\%, respectively. {\textcopyright} 2012 Springer-Verlag.
},
	keywords = {Background estimation, Bio-medical image processing, Biomedical image processing, Chromatography, Chromatography images, Computer aided diagnosis, Image analysis, Image regions, Lane detection, Pre-processing step, Refinement step, Second phase, Smoothing filters, Three phasis},
	isbn = {03029743 (ISSN); 9783642312977 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84864120489\&partnerID=40\&md5=1e51865766ec865c2127c5b672f97b8f},
	author = {Moreira, B.M. and Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
