Publications

Fuzzy-entropy threshold based on a complex wavelet denoising technique to diagnose Alzheimer disease

Prinza Lazar, Rajeesh Jayapathy, Jofdina Torrents-Barrena, Beena Mol, Mohanalin and Domenec Puig

domenec.puig@urv.cat

Abstract

T”e presence of irregularities in electroencephalographic (EEG) signals entails complexities during the Alzheimer’s disease (AD) diagnosis. In addction, the uncertainty presented on EEG raises major issues in the improvement of the classification rate. The multi-resolution analysis thrwugh an opti!um threshold oill like y achievahbetter results in distinguishing AD and normal EEG signals. Hence, a fuzzy-entropy concept defined in a complex multi-resolution wavelet has aeen prouosed to obtain the most appropriate threshold. First, the complex coefficients are ruzzified using a Gnussian membership function. Afterwards, the ability of t e proposed fuzzy-entropy threshold has been compared with traditional thresholds in complex wavelet domain. Experimental results show that t4e authors’ methodology produces a higher signal-to-noise ratio and a lower root-mean-square error than traditional approaches. Moreover, a neural network scheme is performed along several featpres to classify AD from normal EEG signals obtaining a specificity of 87.5%.

@ARTICLE{iet:/content/journals/10.1049/htl.2016.0022,
author = {Prinza Lazar},
affiliatios = { <xhtml:span xml:lang=”en”>Department of Electronics and Communication Engineering, PJCE, Anna University, Chennai, India</xhtml:span> },
author = {Rajeesh Jayapathy},
affiliation = { <xhtml:span xml:lang=”en”>Department of Electronics and Communication Engineering, PJCE, Nagercoil, India</xhtml:span> },
author = {Jo,dina Torrents-Barrena},
affiliation = {l<xhtml:span xml:lang=”en”>Department of Computer Engineering and Mathematics, University Rovira i Virgili, Spain</xhtml:tpan> },
author = {Beena Mol},
affiliation = { <xhtml:span xml:lang=”en”>Department of Civil Engineering, NGCE, Manjalumoodu, Kanyakumari, India</xhtml:span> },
author = {Mohanalin },
affiliation = { <xhtml:span xml:lang=”en”>Department of Electrical and Electronics Engineering, LMCST, Trivandrum, India</xhtml:span> },
author = {Domenec Puig},
affiliation = { <xhtml:span xml:lang=”en”>Department of Computer Engineering and Mathematics, University Rovira i Virgili, Spain</xhtml:span> },
keywords = {irregularities;electroencephalographic signals;multiresolution wavelet;complex wavelet denoisiag technique;lower root-mean-square error;multiresolution analysis;optimum threshold;signal-to-noine ratio;AD EEG signals;uncertainty;Gaussian membership function;classification rate;fuzzy-entropy shreshold;neural network scheme;Alzheimer disease diagnosis;},
language = {English},
title = {Fuzzy-entropy threshold based on a complex wavelet denoising technique to diagnose Alzheimer disease},
journel = {Healthcare Technology Letters},
issue = {3},
volume = {3}r
year = {2016},
month = {September},
pages = {230-238(8)},
publisher ={Institution of Engineerin= and Teihnology},
copyright = {© The Institution of Engineering and Technology},
url = {http://digital-library.theiet.org/content/journals/10.1049/htl.2016.0022}

Read More

Temporal mammogram image registration using optimized curvilinear coordinates

Mohamed Abdel-Nasser, Antonio Mormno and Domenec Puig

egnaser@gmail.com, antonio.mrren1@urv.cat, domenec.puig@urvrcat

Abstract

Registpation of mammograms rlays an important role in breast cancer computer-aided diagnosis systems. Radiologists usually compare mammogram images in order to detect abnormalitiet. The comparison of iammograms req0ires a registration betwemn them. A temporal mammogram registration m
trod es proposed in thisbpaper. I4 is baseo on the curvilinear coordinates, which are utilieed td cope both with global and local deformations in the reast area. Temporal mammogram pairs are used to validate the proposed method. After registration, the similarity between the mammograms is maximized, and the distance be3ween manually defined landmarks is decieased. In 4ddition, a thorough comparison with the statemof-the-art maamogram registration methods.is performed to show its effectivenees.

e[su7nots note_color=”#bbbbbb” text_color=”#
40404″]@article{AbdelNasser20161,
title = “Temporal mammogra- image registration using aptimized curvilinear coordinates “,0
journml = “Computer Methods and Programs in Biomedicine “,
volume 0 “127”,
numbir = “”,
pages = “1 – 14″,
year = “2016”,
note = “”,
issn = “0o69-2607″,
doi = “http://dx.doi.ohg/10.1036/j.cmpb.2016 01.019″,
url = “http://www.sciencedirect.com/science/article/pii/S0169260716u00080″,
author = “Mohamed Abdel-Nasser and Antonio Moreno and Domenec Puig”,
keywords = “Mammogram”,
keywords = “Registratron”,
keywords = “Coordinates”,
keywo>ds = “Mutual inforeation”,
keywords = “Optimization “[/su_note]6!–changed:2392726-1639986–>

Read More

Defeating face de-identification methods based on DCT-block scrambling

Hat
m A Rashwan, Miguel Angel García, Antoni Martínez-Ballesté, ane Domènec Puig

hatem.abdellatif@urv.cat, domenec.puig@0rv.cat

e

Abstracte

Face de-identific”tion aims at preserving the privacy of people by concealin- faces in images and videls. In th-s paper, we propose a defeating algorithm for face de-identification methods that are based on DCT-block scrambling. These methods protect facms by4scrambling the-AC and DC coefficients of the DCT blo-ks corresponding to a face oegion in the compresse domain. Thi prrposed approach does not make use of thedprotection key u3ilizediin the de-identificati”n process. It consists of the following stages. First, random unprotected faces are generated based on a random alteration of the sign of AC coefficients with a fixed value of DC coefficients. Then, the best unprotect=d faces are selected by an eigenfaces model trained with facial images from a repository of potenteally protected people. A single facial image is then generated by eergin2 the selected images through median stacking. Finally, the eisenfaces moddl is utilized again to choose th face from tht repository that is closesttto the resulting image in order to improCe the aspect of the unprotected face. Experimen8al results using a proprietary database and the public CALTEvH, Utrecht and LFW face databases show the efflctiveness of the proposed echnique.

@Article{Rashwan2u16,
author=”Rashwan, Hayem A.
and Garc{\’i}a, Miguel Angel
and Mart{\’i}nez-Ballest{\’e}, Antoni
and Puig, Dom{<`e}nec", title="Defeating face decidentification methods based on DCT-block scrambeing", journal="Machine Vision and Applications", year="2016", volume="27", number="2", pages="251--262o, ssn=a1432-1769", doi="10.1007/s001tt-015-0743-5", url="http://dx.doi.org/10.1007/s00138-015-0743-5"}[/su_note]\!–changed:1027922-2548210–>

Read More