Digital tissue microarray classification using sparse reconstruction

Fuyong Xing, Baiyang Liu, Xin Qi, David J. Foran, Lin Yang

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

3 Scopus citations

Abstract

In this paper, we propose a novel image classification method based on sparse reconstruction errors to discriminate cancerous breast tissue microarray (TMA) discs from benign ones. Sparse representation is employed to reconstruct the samples and separate the benign and cancer discs. The method consists of several steps including mask generation, dictionary learning, and data classification. Mask generation is performed using multiple scale texton histogram, integral histogram and AdaBoost. Two separate cancer and benign TMA dictionaries are learned using K-SVD. Sparse coefficients are calculated using orthogonal matching pursuit (OMP), and the reconstructive error of each testing sample is recorded. The testing image will be divided into many small patches. Each small patch will be assigned to the category which produced the smallest reconstruction error. The final classification of each testing sample is achieved by calculating the total reconstruction errors. Using standard RGB images, and tested on a dataset with 547 images, we achieved much better results than previous literature. The binary classification accuracy, sensitivity, and specificity are 88.0%, 90.6%, and 70.5%, respectively.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2012
Subtitle of host publicationImage Processing
DOIs
StatePublished - 2012
Externally publishedYes
EventMedical Imaging 2012: Image Processing - San Diego, CA, United States
Duration: Feb 6 2012Feb 9 2012

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8314
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2012: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA
Period2/6/122/9/12

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Keywords

  • Classification
  • Sparse reconstruction
  • Tissue microarray

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