Patched Completed Local Binary Pattern is an Effective Method for Neuroblastoma Histological Image Classification

  • Soheila Gheisari
  • Daniel R. Catchpoole
  • Amanda Charlton
  • Paul J. Kennedy
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 845)

Abstract

Neuroblastoma is the most common extra cranial solid tumour in children. The histology of neuroblastoma has high intra-class variation, which misleads existing computer-aided histological image classification methods that use global features. To tackle this problem, we propose a new Patched Completed Local Binary Pattern (PCLBP) method combining Sign Binary Pattern (SBP) and Magnitude Binary Pattern (MBP) within local patches to build feature vectors which are classified by k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifiers. The advantage of our method is extracting local features which are more robust to intra-class variation compared to global ones. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our experiments show the proposed method improves the weighted average F-measure by 1.89% and 0.81% with k-NN and SVM classifiers, respectively.

Keywords

Neuroblastic tumour Neuroblastoma Classification Binary pattern Local patch Image analysis Computer-Aided Diagnosis (CAD) 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Soheila Gheisari
    • 1
  • Daniel R. Catchpoole
    • 2
  • Amanda Charlton
    • 3
  • Paul J. Kennedy
    • 1
  1. 1.Faculty of Engineering and Information Technology, Centre for Artificial IntelligenceUniversity of Technology SydneyUltimoAustralia
  2. 2.Biospecimens Research and Tumour Bank, Children’s Cancer Research UnitThe Kids Research Institute, The Children’s Hospital at WestmeadWestmeadAustralia
  3. 3.The Children’s Hospital at WestmeadWestmeadAustralia

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