Patch Clustering for Representation of Histopathology Images

Wafa Chenni, Habib Herbi, Morteza Babaie, Hamid R. Tizhoosh

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


Whole Slide Imaging (WSI) has become an important topic during the last decade. Even though significant progress in both medical image processing and computational resources has been achieved, there are still problems in WSI that need to be solved. A major challenge is the scan size. The dimensions of digitized tissue samples may exceed 100,000 by 100,000 pixels causing memory and efficiency obstacles for real-time processing. The main contribution of this work is representing a WSI by selecting a small number of patches for algorithmic processing (e.g., indexing and search). As a result, we reduced the search time and storage by various factors between (50%–90%), while losing only a few percentages in the patch retrieval accuracy. A self-organizing map (SOM) has been applied on local binary patterns (LBP) and deep features of the KimiaPath24 dataset in order to cluster patches that share the same characteristics. We used a Gaussian mixture model (GMM) to represent each class with a rather small (10%–50%) portion of patches. The results showed that LBP features can outperform deep features. By selecting only 50% of all patches after SOM clustering and GMM patch selection, we received 65% accuracy for retrieval of the best match, while the maximum accuracy (using all patches) was 69%.

Original languageEnglish (US)
Title of host publicationDigital Pathology - 15th European Congress, ECDP 2019, Proceedings
EditorsConstantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783030239367
StatePublished - 2019
Event15th European Congress on Digital Pathology, ECDP 2019 - Warwick, United Kingdom
Duration: Apr 10 2019Apr 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11435 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th European Congress on Digital Pathology, ECDP 2019
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Patch Clustering for Representation of Histopathology Images'. Together they form a unique fingerprint.

Cite this