Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment

TL;DR

This paper introduces a deep neural network approach using residual networks to classify and rank distortions in laparoscopic images, improving objective quality assessment crucial for surgical accuracy.

Contribution

It formulates image quality assessment as a multi-label classification problem considering both distortion type and severity, which is a novel approach in this context.

Findings

Effective classification of distortion types and severity levels.

Improved accuracy over existing quality assessment methods.

Demonstrated success on laparoscopic image dataset.

Abstract

Laparoscopic images and videos are often affected by different types of distortion like noise, smoke, blur and nonuniform illumination. Automatic detection of these distortions, followed generally by application of appropriate image quality enhancement methods, is critical to avoid errors during surgery. In this context, a crucial step involves an objective assessment of the image quality, which is a two-fold problem requiring both the classification of the distortion type affecting the image and the estimation of the severity level of that distortion. Unlike existing image quality measures which focus mainly on estimating a quality score, we propose in this paper to formulate the image quality assessment task as a multi-label classification problem taking into account both the type as well as the severity level (or rank) of distortions. Here, this problem is then solved by resorting to a deep neural networks based approach. The obtained results on a laparoscopic image dataset show the efficiency of the proposed approach.