Paper Info
Title
Automatic cell detection and segmentation from H and E stained pathology slides using colorspace decorrelation stretching.
Abstract
Purpose: Automatic cell segmentation plays an important role in reliable diagnosis and prognosis of patients. Most of the state-of-the-art cell detection and segmentation techniques focus on complicated methods to subtract foreground cells from the background. In this study, we introduce a preprocessing method which leads to a better detection and segmentation results compared to a well-known state-of-the-art work. Method: We transform the original red-green-blue (RGB) space into a new space defined by the top eigenvectors of the RGB space. Stretching is done by manipulating the contrast of each pixel value to equalize the color variances. New pixel values are then inverse transformed to the original RGB space. This altered RGB image is then used to segment cells. Result: The validation of our method with a well-known state-of-the-art technique revealed a statistically significant improvement on an identical validation set. We achieved a mean F1-score of 0.901. Conclusion: Preprocessing steps to decorrelate colorspaces may improve cell segmentation performances.
Year
DOI
Venue
2016
10.1117/12.2216507
Proceedings of SPIE
Keywords
Field
DocType
digital pathology,cell segmentation,decorrelation stretch,automated image analysis
Computer vision,Color space,Scale-space segmentation,Segmentation,Computer science,Segmentation-based object categorization,Digital pathology,Image segmentation,RGB color model,Artificial intelligence,Pixel
Conference
Volume
ISSN
Citations 
9791
0277-786X
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Mohammad Peikari1113.03
Anne L. Martel213322.41