Patch-Based Sparse Representation For Bacterial Detection

TL;DR

This paper introduces an unsupervised patch-based sparse representation method for bacterial detection in optical endomicroscopy images, leveraging learned dictionaries and ADMM optimization to identify anomalies without prior bacterial data.

Contribution

The novel approach models background and anomalies separately using sparse coding and fixed dictionaries, enabling unsupervised bacterial detection in medical images.

Findings

Good detection performance on ex vivo lung datasets

High correlation with clinician-identified bacteria counts

Effective anomaly identification without labeled training data

Abstract

In this paper, we propose an unsupervised approach for bacterial detection in optical endomicroscopy images. This approach splits each image into a set of overlapping patches and assumes that observed intensities are linear combinations of the actual intensity values associated with background image structures, corrupted by additive Gaussian noise and potentially by a sparse outlier term modelling anomalies (which are considered to be candidate bacteria). The actual intensity term representing background structures is modelled as a linear combination of a few atoms drawn from a dictionary which is learned from bacteria-free data and then fixed while analyzing new images. The bacteria detection task is formulated as a minimization problem and an alternating direction method of multipliers (ADMM) is then used to estimate the unknown parameters. Simulations conducted using two ex vivo lung datasets show good detection and correlation performance between bacteria counts identified by a trained clinician and those of the proposed method.