Automated image segmentation of haematoxylin and eosin stained skeletal muscle cross-sections

Author:

LIU F.12,MACKEY A.L.34,SRIKUEA R.5,ESSER K.A.6,YANG L.12

Affiliation:

1. Division of Biomedical Informatics; Department of Biostatistics; University of Kentucky; Lexington Kentucky 40536 U.S.A.

2. Department of Computer Science; University of Kentucky; Lexington Kentucky 40536 U.S.A.

3. Institute of Sports Medicine; Department of Orthopaedic Surgery M; Bispebjerg Hospital; Copenhagen Denmark

4. Centre for Healthy Ageing; Faculty of Health and Medical Sciences; University of Copenhagen; Denmark

5. Department of Physiology; Faculty of Science; Mahidol University; Bangkok 10400 Thailand

6. Department of Physiology; Center for Muscle Biology; University of Kentucky; Lexington Kentucky 40536 U.S.A.

Funder

National Center for Research Resources

National Center for Advancing Translational Sciences

Center for Muscle Biology (CMB) at University of Kentucky

MYOAGE

Publisher

Wiley

Subject

Histology,Pathology and Forensic Medicine

Reference35 articles.

1. Active contours without edges;Chan;IEEE Trans. Image Process,2001

2. Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening;Chen;J. Microsc.,2008

3. On active contour models and balloons;Cohen;Comput. Vis. Graph. Image Process,1991

4. Inspiratory muscle training in patients with heart failure and inspiratory muscle weakness: a randomized trial;Dall'Ago;J. Am. Coll. Cardiol,2006

5. A note on the gradient of a multi-image;Di Zenzo;Comput. Vis. Graph. Image Process,1986

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