Wavelets Based on Atomic Function Used in Detection and Classification of Masses in Mammography

Autor:	Volodymyr Ponomaryov, Magally Martinez-Reyes, Jose Luis. Sanchez-Ramirez, Cristina Juarez-Landin, Victor F. Kravchenko
Rok vydání:	2008
Předmět:	Artificial neural network medicine.diagnostic_test Computer science business.industry Wavelet transform Classification scheme Atomic function Wavelet medicine Mammography Computer vision Artificial intelligence Microcalcification medicine.symptom business Classifier (UML)
Zdroj:	MICAI 2008: Advances in Artificial Intelligence ISBN: 9783540886358 MICAI
DOI:	10.1007/978-3-540-88636-5_28
Popis:	Mammography is considered the most effective method for early detection of the breast cancer. However, it is difficult for radiologists to detect microcalcification (MC) clusters and camouflages masses. The mammograms (MG) images were decomposed into several subimages using Wavelet transform (WT) based on classical and novel class Wavelets using atomic function for reducing the volume of data in the classification stage. Various regions of interest (ROIs) in the MG images were selected where input data for multilayer artificial neural network (ANN) type classifier are formed applying the WT. We used different patterns to classify the normal, MC, spiculated and circumscribed masses ROIs. The detection performance has been evaluated on MG images from the Mammographic Image Analysis Society (MIAS) database. The proposed classification scheme was shown good performance in detecting the MC clusters and masses with acceptable classification.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::147d8cec058e963c6428d439b40b48de https://doi.org/10.1007/978-3-540-88636-5_28 Zobrazit plný text záznamu