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Training based cell detection from bright-field microscope images

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publication2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA). September 7-9, 2015, Zagreb, Croatia
Number of pages5
ISBN (Print)978-1-4673-8032-4
Publication statusPublished - 2015
Publication typeA4 Article in a conference publication
EventInternational Symposium on Image and Signal Processing and Analysis - , Croatia
Duration: 1 Jan 2015 → …


ConferenceInternational Symposium on Image and Signal Processing and Analysis
Period1/01/15 → …


This paper proposes a framework for cell detection from bright-field microscope images. The method is trained using manually annotated images, and it uses Support Vector Machine classifiers with Histogram of Oriented Gradient features. The performance of the method is evaluated using 16 training and 12 test images with altogether 10736 human prostate cancer cells. Both the implementation and the annotated image database are released for download. The experiments consider various parameters and their effect on performance, and reaches accurate detection results with cross-validated AUC over 0.98, and mean relative deviation of 9 % from manually counted annotations in the growth curve over six days.


  • Biomedical imaging, Image segmentation, Manuals, Cell Detection, Growth Curve, Histogram of Oriented Gradients, Supervised Learning, Support Vector Machine

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