A compact representation of histopathology images using digital stain separation and frequency-based encoded local projections

Alison K. Cheeseman, Hamid Tizhoosh, Edward R. Vrscay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationImage Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings
EditorsFakhri Karray, Alfred Yu, Aurélio Campilho
PublisherSpringer Verlag
Pages147-158
Number of pages12
ISBN (Print)9783030272715
DOIs
StatePublished - 2019
Event16th International Conference on Image Analysis and Recognition, ICIAR 2019 - Waterloo, Canada
Duration: Aug 27 2019Aug 29 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11663 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Image Analysis and Recognition, ICIAR 2019
Country/TerritoryCanada
CityWaterloo
Period8/27/198/29/19

Keywords

  • Digital histopathology
  • Digital image retrieval and classification
  • Digital stain separation
  • Encoded local projections (ELP)
  • Radon transform

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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