A Latent Source Model for Patch-Based Image Segmentation

TL;DR

This paper introduces a probabilistic model for patch-based image segmentation, providing theoretical guarantees for methods like nearest-neighbor and weighted voting, and proposes a new algorithm that unifies existing approaches.

Contribution

It offers the first theoretical performance analysis for patch-based segmentation methods and develops a new algorithm that generalizes many existing techniques.

Findings

Provides a performance guarantee for patch-based segmentation methods.

Derives a new algorithm that unifies existing patch-based segmentation approaches.

Demonstrates the effectiveness of the algorithm through theoretical analysis.

Abstract

Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm.