Computer Aided Classification of Mammographic Tissue Using Shapelets and Support Vector Machines

Autor:	George Apostolopoulos, Athanasios Koutras, Ioanna Christoyianni, Evaggelos Dermatas
Rok vydání:	2014
Předmět:	Computer science business.industry Zernike polynomials Feature extraction Pattern recognition Support vector machine symbols.namesake Mammographic image Decomposition (computer science) Computer-aided symbols Radial basis function Artificial intelligence business
Zdroj:	Artificial Intelligence: Methods and Applications ISBN: 9783319070636 SETN
DOI:	10.1007/978-3-319-07064-3_44
Popis:	In this paper a robust regions-of-suspicion (ROS) diagnosis system on mammograms, recognizing all types of abnormalities is presented and evaluated. A new type of descriptors, based on Shapelet decomposition, derive the source images that generate the observed ROS in mammograms. The Shapelet decomposition coefficients can be used efficiently to detect ROS areas using Support-Vector-Machines (SVMs) with radial basis function kernels. Extensive experiments using the Mammographic Image Analysis Society (MIAS) database have shown high recognition accuracy above 86% for all kinds of breast abnormalities that exceeds the performance of similar decomposition methods based on Zernike moments presented in the literature by more than 8%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8746980a256c1adfd8c6b1747610833e https://doi.org/10.1007/978-3-319-07064-3_44 Zobrazit plný text záznamu