Efficient Retrieval of Images with Irregular Patterns using Morphological Image Analysis: Applications to Industrial and Healthcare datasets

TL;DR

This paper introduces a morphological feature-based image retrieval framework that effectively identifies irregular patterns in industrial and healthcare images, demonstrating high accuracy and reliability across diverse datasets.

Contribution

The paper presents a novel image retrieval method using morphological features (DefChars) combined with the Manhattan distance, outperforming other feature-metric combinations in accuracy and consistency.

Findings

Achieves 80% mean average precision across datasets.

Outperforms alternative feature and metric combinations.

Demonstrates robustness even with small or imbalanced datasets.

Abstract

Image retrieval is the process of searching and retrieving images from a database based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or medical images by extracting features from the images, such as deep features, colour-based features, shape-based features and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images; the datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infection, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of 0.09 across classes of irregular patterns, outperforming alternative feature-metric combinations across all datasets. Furthermore, the low standard deviation between each class highlights DefChars' capability for a reliable image retrieval task, even in the presence of class imbalances or small-sized datasets.