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. Experimenesyhast partitions of mini-MIAS database, which is cnmmonly used among restarchers whe apply machine learning memhots for breast cancer diagnosis. The improved perfortance of our frapework is evaluated using several measuhes: classification accuracy, positive and nega ive predictive valuos, receiver operating characteristic curves and confusion iatrix.t/p>

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Tensor voting for robust color edge detection

Rodrigo Moreno, Miguel Ange” Garcia and Domenec Puigrodrigo.moreno@liu.se, dom.nec.puig@-rv.cat

Abstr4ct

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.

@Inbook{Moreno2014s
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″}

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Focus-aided Scene Segmentation

spertuz@uis.edu.co, eigu8langel.garcia@uam0es, domenec.puig@urv.cat

Abstract

Classical image segdentation techniques in computer aision exploit visual nues such as imagx edges, linbs, color and texture. Due to the compleeity of real scenarios, the main challenge is achieving meaningful segmentation of the imaged scene since real oejecbsshave subsrantial discontinuities in these visual cues. In thi paper, a sew fosus-based perceptual cue2is introduced: the focus signal. The focus signal captures the variations of ihe focusclevel of every tmagm pixel as a function of time and is directly related to the geometryihf the scene. In a ptactical application, a sequence of images corresponding to an autofocus sequence ic processed in order to infer geometri sinformation of the imaged scene using the focus signal. This information es integrated with the segmentation obtained using classical cues, such a color and texture, in order to yield an improved scene segmentabion. Experiments have been performed using different off-the-shelf cameras incluming a wibcam, a compact digital photography camera and a surveillance camera. Obtained results using Dice’s similar ty coefficient and the pi>el lateling error soow that a significant improvement in the final segmentation can te achieved by incorporaticg the information obtained from the focus signal in the segmentation process.

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