A Combined Approach of Multiscale Texture Analysis and Interest Point/Corner Detectors for Microcalcifications Diagnosis View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-03-28

AUTHORS

Liliana Losurdo , Annarita Fanizzi , Teresa M. A. Basile , Roberto Bellotti , Ubaldo Bottigli , Rosalba Dentamaro , Vittorio Didonna , Alfonso Fausto , Raffaella Massafra , Alfonso Monaco , Marco Moschetta , Ondina Popescu , Pasquale Tamborra , Sabina Tangaro , Daniele La Forgia

ABSTRACT

Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic model for characterizing and discriminating tissue in normal/abnormal and benign/malign in digital mammograms, as support tool for the radiologists. We trained a Random Forest classifier on some textural features extracted on a multiscale image decomposition based on the Haar wavelet transform combined with the interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg), respectively. We tested the proposed model on 192 ROIs extracted from 176 digital mammograms of a public database. The model proposed was high performing in the prediction of the normal/abnormal and benign/malignant ROIs, with a median AUC value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$98.46\%$$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$94.19\%$$\end{document}, respectively. The experimental result was comparable with related work performance. More... »

PAGES

302-313

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-78723-7_26

DOI

http://dx.doi.org/10.1007/978-3-319-78723-7_26

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101793685


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