An Unsupervised Ensemble-based Markov Random Field Approach to Microscope Cell Image Segmentation

TL;DR

This paper introduces an unsupervised ensemble-based Markov Random Field method for segmenting microscope cell images, leveraging bit plane slicing and pixelwise voting to improve robustness and competitiveness.

Contribution

It presents a novel unsupervised segmentation approach combining Markov Random Fields with bit plane slicing and ensemble voting, not previously explored in this context.

Findings

Achieved competitive results on a public microscope cell image database.

Demonstrated robustness of segmentation through ensemble voting.

Validated approach's effectiveness compared to manual segmentation.

Abstract

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.