Detection of leukocytes in contact with the vessel wall from in vivo microscope recordings using a neural network

被引:17
作者
Egmont-Petersen, M [1 ]
Schreiner, U
Tromp, SC
Lehmann, TM
Slaaf, DW
Arts, T
机构
[1] Maastricht Univ, Dept Biophys, Maastricht, Netherlands
[2] Rhein Westfal TH Aachen, Inst Med Informat, Aachen, Germany
[3] Maastricht Univ, Dept Physiol, Maastricht, Netherlands
关键词
leukocyte detection; microcirculation; model-based image processing; nonlinear filtering; object recognition; shape characterization; stochastic model;
D O I
10.1109/10.846689
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Leukocytes play an important role in the host defense as they may travel from the blood stream into the tissue in reacting to inflammatory stimuli. The leukocyte-vessel wall interactions are studied in post capillary vessels by intraviral video microscopy during in vivo animal experiments. Sequences of video images are obtained and digitized with a frame grabber. A method for automatic detection and characterization of leukocytes in the video images is developed. Individual leukocytes are detected using a neural network that is trained with synthetic leukocyte images generated using a novel stochastic model. This model makes it feasible to generate images of leukocytes with different shapes and sizes under various lighting conditions. Experiments indicate that neural networks trained with the synthetic leukocyte images perform better than networks trained with images of manually detected leukocytes. The best performing neural network trained with synthetic leukocyte images resulted in an 18% larger area under the ROC curve than the best performing neural network trained with manually detected leukocytes.
引用
收藏
页码:941 / 951
页数:11
相关论文
共 27 条
[1]   MULTIDIMENSIONAL INDEXING FOR RECOGNIZING VISUAL SHAPES [J].
CALIFANO, A ;
MOHAN, R .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1994, 16 (04) :373-392
[2]   ACTIVE SHAPE MODELS - THEIR TRAINING AND APPLICATION [J].
COOTES, TF ;
TAYLOR, CJ ;
COOPER, DH ;
GRAHAM, J .
COMPUTER VISION AND IMAGE UNDERSTANDING, 1995, 61 (01) :38-59
[3]   NEAREST NEIGHBOR PATTERN CLASSIFICATION [J].
COVER, TM ;
HART, PE .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1967, 13 (01) :21-+
[4]   IMAGE-PROCESSING AND NEURAL NETWORKS FOR RECOGNITION OF CARTOGRAPHIC AREA FEATURES [J].
DEKRUGER, D ;
HUNT, BR .
PATTERN RECOGNITION, 1994, 27 (04) :461-483
[5]   INFLUENCE OF PLATELET VESSEL WALL INTERACTIONS ON LEUKOCYTE ROLLING INVIVO [J].
EGBRINK, MGAO ;
TANGELDER, GJ ;
SLAAF, DW ;
RENEMAN, RS .
CIRCULATION RESEARCH, 1992, 70 (02) :355-363
[6]   Detection of bone tumours in radiographic images using neural networks [J].
Egmont-Petersen, M ;
Pelikan, E .
PATTERN ANALYSIS AND APPLICATIONS, 1999, 2 (02) :172-183
[7]   Recognition of radiopaque markers in X-ray images using a neural network as nonlinear filter [J].
Egmont-Petersen, M ;
Arts, T .
PATTERN RECOGNITION LETTERS, 1999, 20 (05) :521-533
[8]  
EGMONTPETERSEN M, 1996, AACH WORKSH BILDV ME, P209
[9]   ON THE APPROXIMATE REALIZATION OF CONTINUOUS-MAPPINGS BY NEURAL NETWORKS [J].
FUNAHASHI, K .
NEURAL NETWORKS, 1989, 2 (03) :183-192
[10]  
Gonsalez R.C., 1992, DIGITAL IMAGE PROCES