Texture feature extraction for tumor detection in mammographic images

Autor:	Rabab K. Ward, B. Palcic, J. Morgan-Parkes, M. Sameti
Rok vydání:	2002
Předmět:	Contextual image classification business.industry Feature extraction Pattern recognition Image segmentation Data set Image texture Computer-aided diagnosis Feature (computer vision) Computer vision Segmentation Artificial intelligence business Mathematics
Zdroj:	1997 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM. 10 Years Networking the Pacific Rim, 1987-1997.
DOI:	10.1109/pacrim.1997.620388
Popis:	A set of texture features are extracted from segmented regions of digitized mammograms for classification of masses from normal regions. The mass detection algorithm consists of two steps. In the first step, the algorithm employs a segmentation method based on the fuzzy sets theory to divide a mammogram into different regions and produces region(s) of mass candidates. In the second step, discrete texture features are calculated for the area of each mass candidate. Two of those feature were sufficient to produce a 94% true-positive detection rate with a low 0.24 false-positives per image for a data set of 35 mammograms with a malignant mass in each.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0b7cdbd35028aa8411ef77d00819feb0 https://doi.org/10.1109/pacrim.1997.620388 Zobrazit plný text záznamu