Authors:
Ghada Ouddai
;
Ines Hamdi
and
Henda Ben Ghezala
Affiliation:
RIADI Laboratory, National School of Computer Science (ENSI), University of La Manouba, La Manouba, Tunisia
Keyword(s):
Histopathological Image Processing, Feature Extraction, Binary Robust Invariant Scalable (BRISK), Oriented FAST, Rotated BRIEF (ORB), DAISY Descriptor, Bag-of-Features (BoF), Machine Learning.
Abstract:
Medical data analysis is one of the most emergent fields over the past decades. In Digital histopathology, images are analysed, mainly, to detect disease or tumors and identify their types and grade. One of the most used practices in this field is the feature extraction. In this paper, we propose the application of BRISK, ORB and BRISK/DAISY on RGB histological images. The purpose of this work is to recognise the breast tumor type (benign or malignant). These features extractors are combined with BoF by kmeans and SVM. A limited amount of images is used during the training of the system. Out of the three methods, Color-BRISK/BoF/SVM solution gave the best accuracy value (72.5%) while Color-ORB/BoF/SVM was the fastest.