# A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections

## TL;DR

This paper introduces a frequency-based local projection encoding method for histopathology images that reduces data size and computation time, improving image retrieval and classification accuracy, especially when applied to digitally separated stain components.

## Contribution

It proposes a modified encoded local projections algorithm capturing local frequency info, enhancing efficiency and accuracy in histopathology image analysis.

## Key findings

- Outperforms original ELP in image retrieval tasks.
- Achieves classification accuracy comparable to deep learning methods.
- Reduces histogram size and computation time significantly.

## Abstract

In recent years, histopathology images have been increasingly used as a diagnostic tool in the medical field. The process of accurately diagnosing a biopsy sample requires significant expertise in the field, and as such can be time-consuming and is prone to uncertainty and error. With the advent of digital pathology, using image recognition systems to highlight problem areas or locate similar images can aid pathologists in making quick and accurate diagnoses. In this paper, we specifically consider the encoded local projections (ELP) algorithm, which has previously shown some success as a tool for classification and recognition of histopathology images. We build on the success of the ELP algorithm as a means for image classification and recognition by proposing a modified algorithm which captures the local frequency information of the image. The proposed algorithm estimates local frequencies by quantifying the changes in multiple projections in local windows of greyscale images. By doing so we remove the need to store the full projections, thus significantly reducing the histogram size, and decreasing computation time for image retrieval and classification tasks. Furthermore, we investigate the effectiveness of applying our method to histopathology images which have been digitally separated into their hematoxylin and eosin stain components. The proposed algorithm is tested on the publicly available invasive ductal carcinoma (IDC) data set. The histograms are used to train an SVM to classify the data. The experiments showed that the proposed method outperforms the original ELP algorithm in image retrieval tasks. On classification tasks, the results are found to be comparable to state-of-the-art deep learning methods and better than many handcrafted features from the literature.