title: Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach.
abstract: BACKGROUND
  According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations.
  
  
  OBJECTIVE
  The aim is to investigate the various texture features and Hierarchical Model and X (HMAX) biologically inspired approach for mitosis detection using machine-learning techniques.
  
  
  METHODS
  We propose an approach that assists pathologists in automated mitosis detection and counting. The proposed method, which is based on the most favorable texture features combination, examines the separability between different channels of color space. Blue-ratio channel provides more discriminative information for mitosis detection in histopathological images. Co-occurrence features, run-length features, and Scale-invariant feature transform (SIFT) features were extracted and used in the classification of mitosis. Finally, a classification is performed to put the candidate patch either in the mitosis class or in the non-mitosis class. Three different classifiers have been evaluated: Decision tree, linear kernel Support Vector Machine (SVM), and non-linear kernel SVM. We also evaluate the performance of the proposed framework using the modified biologically inspired model of HMAX and compare the results with other feature extraction methods such as dense SIFT.
  
  
  RESULTS
  The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for an International Conference on Pattern Recognition (ICPR) 2012 contest. The proposed framework achieved 76% recall, 75% precision and 76% F-measure.
  
  
  CONCLUSIONS
  Different frameworks for classification have been evaluated for mitosis detection. In future work, instead of regions, we intend to compute features on the results of mitosis contour segmentation and use them to improve detection and classification rate.
authors.affiliation: University of Joseph Fourier, Grenoble, France.
authors.forename: Humayun
authors.initials: H
authors.lastname: Irshad
authors.forename: Sepehr
authors.initials: S
authors.lastname: Jalali
authors.forename: Ludovic
authors.initials: L
authors.lastname: Roux
authors.forename: Daniel
authors.initials: D
authors.lastname: Racoceanu
authors.forename: Lim Joo
authors.initials: LJ
authors.lastname: Hwee
authors.forename: Gilles Le
authors.initials: GL
authors.lastname: Naour
authors.forename: Frédérique
authors.initials: F
authors.lastname: Capron
keywords: Classification; Hierarchical Model and X; Scale-invariant feature transform; histopathology; mitosis detection; texture analysis