Evaluation and benchmark for biological image segmentation

This paper describes ongoing work on creating a benchmarking and validation dataset for biological image segmentation. While the primary target is biological images, we believe that the dataset would be of help to researchers working in image segmentation and tracking in general. The motivation for creating this resource comes from the observation that while there are a large number of effective segmentation methods available in the research literature, it is difficult for the application scientists to make an informed choice as to what methods would work for her particular problem. No one single tool exists that is effective on a diverse set of application contexts and different methods have their own strengths and limitations. We describe below three different classes of data, ranging in scale from subcellular to cellular to tissue level images, each of which pose their own set of challenges to image analysis. Of particular value to the image processing researchers is that the data ...

• Type: Conference
• Year: 2008
• Where: ICIP
• Authors: Elisa Drelie Gelasca, Jiyun Byun, Boguslaw Obara, B. S. Manjunath
• Keywords: Associated Ground Truth, Biological Image Segmentation, Effective Segmentation Methods, ICIP 2008, Image Processing, Image Processing Researchers, Validation Dataset