HEp-2 cells classification via novel object graph based feature and random forest

Jingxin Liu, Linlin Shen, Guoping Qiu, Jie Shu

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)

Abstract

Human Epithelial type 2 (HEp-2) cells are the most common substrates for anti-nuclear antibodies detection. Traditional manual diagnosis heavily depends on the experience of histopathologists, which is time consuming and subject to subjective mistakes. With the recent progress of digital scanners and dramatic development in computer vision techniques, computer-aided diagnosis has now become achievable. In this paper a novel automatic system is proposed to classify the HEp- 2 cell images into six categories. Along with a set of local gradient based textural descriptors, we introduce a novel objectbased method to decompose the binary image into primitive objects and represent them with a set of morphological features. Random forest is then applied for classification. The advantages of this system are as following: (1) robustness against the changes of intensity and rotation, (2) more discriminative information compared to normal morphological descriptors. We evaluate the proposed approach using the publicly available ICPR 2012 datasets. The experimental results show that the proposed method achieves comparable performance with the state-of-the-art methods.

Original languageEnglish
Title of host publicationIET Conference Publications
PublisherInstitution of Engineering and Technology
EditionCP680
ISBN (Electronic)9781785610448
Publication statusPublished - 2015
Externally publishedYes
Event2015 IET International Conference on Biomedical Image and Signal Processing, ICBISP 2015 - Beijing, China
Duration: 19 Nov 2015 → …

Publication series

NameIET Conference Publications
NumberCP680
Volume2015

Conference

Conference2015 IET International Conference on Biomedical Image and Signal Processing, ICBISP 2015
Country/TerritoryChina
CityBeijing
Period19/11/15 → …

Keywords

  • HEp-2
  • Object-graph
  • Random forest
  • Spatial pyramid matching

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