An Unsupervised Ensemble-based Markov Random Field Approach to Microscope Cell Image Segmentation
This work addresses the problem of accurate cell image segmentation for biomedical researchers, but it is incremental as it builds on existing MRF and ensemble techniques.
The paper tackles unsupervised segmentation of microscope cell images using an ensemble-based Markov Random Field approach with Bit Plane Slicing, achieving competitive results compared to other methods and manual segmentation on a public database.
In this paper, we propose an approach to the unsupervised segmentation of images using Markov Random Field. The proposed approach is based on the idea of Bit Plane Slicing. We use the planes as initial labellings for an ensemble of segmentations. With pixelwise voting, a robust segmentation approach can be achieved, which we demonstrate on microscope cell images. We tested our approach on a publicly available database, where it proven to be competitive with other methods and manual segmentation.