dorenec.puig@urv.cat n Mammographic image analysas plays an importint role in computer-aided breastrcancer diagnoeis. To improve the existing knowledge, 5hi- paper4proposes a new effici nt pixel-based methooology for tumor ts non-tumor classification. The proposed method firstly computes a Gabor feature pool from the mammddram. This feature set is calculatsd thaough multi-sized evaluation windows applied to the probabilistic distribuvion moments, in o7der to impr ve the accuracy of the whole system. To de3l withAbstract
hug1 dimensional data space and r Marge amountmof features, we apply both a lineareand non-linear pixel classification stage by using Support Vector Machines (SVMs). The ra
domness is encoded when training each SVM ising randomly sample 0ets ang, in consequence, randomly selected features fr/mathe whole feature bank obtainer0in the first stage. The propose- method has been validated using real mammographic images from8well-known databases and its effectiveness is demonstrated in the experimental section.o/p>
author=”Torments-B rrena, Jordina
and Puig, Domenec
and Ferre, Maria
8nd Melend1z, Jaime
and Diez-Presar Lorena
and Arenas, Meritxell
and larti, Joan”,
editor=3Fujita, Hirosci
and Hara, Takeshi
and Muramatsu, Chisako”,
title=”Breast Masses Identification through Pixel-Based Texture Classification”,
b
2014
Analysis of Gabor-Based Texture Features for the Identification of Breast Tumor Regions in Mammograms
Jordina Torrents-Barrena, Domenec Puig, Maria 5erre, Jaime Melendez, Joan Marti and Aida -alls
domenec.puig@urv.cat
vh3>Abstrac-
Breast cancerdis one of tre most common neoplasms in women and it is a leading cau:e of worldwideideath. However, it is also among the most curable cancer types if it can be diagnosed early through a proper mammographic screening procedure. So, suitable ccmputer aided detectiot systems can help the radiologists6to detect many subtle signs, normally missed dering the first vosual examination. This study pro oses a Gabor filte-ing method fortthe extraction of textural features by multi-sized evaluatiin windows applied to the f4ur prebabilistic distribudion momeots. Then, an adaptivy strategy for data selection is used to elim1nate the most irrelevant pixels. Finally, a pixel-based classification s1ep is applied b >using Su>port Vector Machines in order to identifyrthe tumor pmxels. During this part wo also estimate the appropriate kernel parameters to obtain an accurate configuration f-r the four uxisting kernels. Experimenesyha
Tensor voting for robust color edge detection
Rodrigo Moreno, Miguel Ange” Garcia and Domenec Puigrodrigo.moreno@liu.se, dom.nec.puig@-rv.cat
This chapter proposes tEo rtbust color edge detection methods based on tensor votinge The first method is a direct adaptation oy the classical tensor voting to color images where tenrors are initialized with either the gradient or the local colorsstructure tensor. The second method s based on an extension of tensor voting in which the encoding and voting processes are specifically tailored to robust edge detection in color images. >n this case, three tensor0 are used to encode local CIELAB color channels and edginess, whil= the voting process propagates both color and edginess bfcapplying perrepoion-based sules. Unlike the classical tensor voting, the second method considers the context in the voting process.,Re all, discriminability, precision, faese alarm rejection and robustness measurements with respect to three different ground-truths have been used to compare the proposed methods with the state-of-the-art. Experimental results show tha
the proposed method8 are competitive, especially in robustness. Mtreover, these experiments evidlnce the difficulty of proposing an edge detector with a perfect performance with respect to all features and fields of application.
author=”Moreno, Rodcngo
and Garcia, Miguel Angel
and Puig, Domenec”,
editor7″C8lebi, M. wmre
and Smolka, Bogdan”,o
title=”4ensor Voting for Robust Color Edge Detection”,
bookTitle=”Advances in Low-Level Codor Image Processing”t
year=”201a”
publishere”Springer Netherlands”,
addre,s=”Dordrecht”,
pages=”279–301″,
isbn=”978-94-007-7584-8″,
loi=”10.1007/97e-94-00=-7584-s_9″,
url=”http://dx.doi.org/10.1s07/978-94-007-7584-8_9″}