Automatic Analysis of Dot Blot Images

被引：5

作者：

Caridade, C. M. R. ^{[1
,2
]}

Marcal, A. R. S. ^{[3
,4
]}

Albuquerque, P. ^{[5
]}

Mendes, M. V. ^{[6
]}

Tavares, F. ^{[3
,5
]}

机构：

[1] Inst Super Engn Coimbra, Coimbra, Portugal

[2] CICGE Ctr Invest Ciencias Geoespaciais, Oporto, Portugal

[3] Univ Porto, Fac Ciencias, P-4100 Oporto, Portugal

[4] INESC TEC, Oporto, Portugal

[5] CIBIO Ctr Invest Biodiversidade & Recursos Genet, Vairao, Portugal

[6] IBMC, Oporto, Portugal

来源：

INTELLIGENT AUTOMATION AND SOFT COMPUTING | 2015年 / 21卷 / 04期

关键词：

Molecular markers; Macroarray; Image processing; Computer vision; OLIGONUCLEOTIDE ARRAY; RAPID DETECTION; HARMFUL ALGAE; IDENTIFICATION; DIFFERENTIATION;

D O I：

10.1080/10798587.2015.1012849

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a method for the automatic analysis of macroarray (dot blot) images. The system developed receives as input a dot blot image, corrects it for grid rotation, identifies the visible markers and provides an evaluation of the status of each marker (ON/OFF). Two experiments were carried out to evaluate the detection and classification stages. A total of 222 test images were produced from 6 original dot blot images, with various rotations, translations, contrast and noise level. Over 7500 markers were identified automatically and compared to manual reference. The RMS error in positioning the molecular marker center was between 1.1 and 3.8 pixels and the marker radius error less than 4%. The automatic classification of markers (ON/OFF) was compared to the classification by 3 human experts, using 10 test images. The overall accuracy evaluated on 5118 markers was 94.0%. For those markers that had the same evaluation by all 3 experts, the classification accuracies were 96.6% (ON) and 95.9% (OFF).

引用

页码：607 / 622

页数：16

共 21 条

[1]

Albuquerque P., 2006, P 2 FEMS C MADR SPAI

[2] DNA signature-based approaches for bacterial detection and identification [J].

Albuquerque, Pedro ;

Mendes, Marta V. ;

Santos, Catarina L. ;

Moradas-Ferreira, Pedro ;

Tavares, Fernando .

SCIENCE OF THE TOTAL ENVIRONMENT, 2009, 407 (12) :3641-3651

[3] Automatic analysis of macroarrays images [J].

Caridade, C. M. R. ;

Marcal, A. R. S. ;

Mendonca, T. ;

Albuquerque, P. ;

Mendes, M. V. ;

Tavares, F. .

2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2010, :6122-6125

[4] Diagnostic Potential of Anti-rNcp-43 Polyclonal Antibodies for the Detection of Neospora caninum [J].

de Sa, Gizele Lima ;

Pacheco, Diene de Borba ;

Monte, Leonardo Garcia ;

Sinnott, Francine Alves ;

Xavier, Marina Amaral ;

Rizzi, Caroline ;

Borsuk, Sibele ;

Aires Berne, Maria Elisabeth ;

Andreotti, Renato ;

Hartleben, Claudia Pinho .

CURRENT MICROBIOLOGY, 2014, 68 (04) :472-476

[5] An oligonucleotide array for the identification and differentiation of bacteria pathogenic on potato [J].

Fessehaie, A ;

De Boer, SH ;

Lévesque, CA .

PHYTOPATHOLOGY, 2003, 93 (03) :262-269

[6] Immunological detection of Phaeoacremonium aleophilum, a fungal pathogen found in esca disease [J].

Fleurat-Lessard, Pierrette ;

Luini, Estelle ;

Berjeaud, Jean-Marc ;

Roblin, Gabriel .

EUROPEAN JOURNAL OF PLANT PATHOLOGY, 2014, 139 (01) :137-150

[7] Rapid detection and differentiation of important Campylobacter spp. in poultry samples by dot blot and PCR [J].

Fontanot, Marco ;

Iacumin, Lucilla ;

Cecchini, Francesca ;

Comi, Giuseppe ;

Manzano, Marisa .

FOOD MICROBIOLOGY, 2014, 43 :28-34

[8]

Guillemin N, 2009, J PHYSIOL PHARMACOL, V60, P91

[9] Evaluation of a digoxigenin-labelled probe for detection of Aeromonas spp. [J].

Khushiramani, R. ;

Girisha, S. K. ;

Karunasagar, I. ;

Karunasagar, I. .

LETTERS IN APPLIED MICROBIOLOGY, 2009, 48 (03) :383-385

[10]

Kraus K., 1993, PHOTOGRAMMETRY FUNDA

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