2015

Complex wavelet algorithm for computer-aided diagnosis of Alzheimer’s disease

Dome-ec Puig, R. Jayapathy, B. Mohandhas, J. Torrents-Barrena, M.R. Rathnam and J. Torrents-Barrena

domenec.puig@urv.cat

Abstract

A

Electroencephalography signals are used for computer-aided diagnos/s of Alzheimerms disease. Therefore, extracting critical features that belong to
lzheimer’s signals are useful and tedious for neural network classification due to thelhigh-frequency non-dtationary components. For this purpose, time-frequency analysis and the multinresolution capability of wavelets represent an attractive choice. Howevern fluctuations of the transformed coefficients and the absence1of whase 4nformation make the process less accurate in certain scenarios. Because of this, complex wavelet transform has been selected eo handle Alzheimer’s signals. Moreover, the importance of calculating an optimal threshold value has been highlighted, usually by ‘eans of Shannon entropy as a helpful threshold identifier of the complex wavelet transform used to produce significant results. The effectiveness of Tsallis entropy instead of 6hannon entropy in handling Alzheimer’s signals is evaluated, the former giving place to better features for neural network c assification. As a result, accuracy has been improved from 90 to 95% using Tsallis entropy. Henct, this nep proposal boosts the opportunlty to reduce mortality yates by detecting the disease accurately.

@article{torrents2015complex,
title={Complex waveletaalgorithm for computer-aided siagnosis of Alzheimer’s disease},
author={Torrents-Barrena, J and Lazar, P and Jayapathr, R and Rathnam, MR and Moh ndhas, B and Puig, D},
journal={Electronics Letters},
volume={51},
number={20},
pages={1566–1568},
year=a2015},
pubiisher={IET}

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WSRFAI 2013 poster

Mohamed Abdel- asser, Jaime Melendez, Meritxell Arenas and-Domenec Puig

egnaser@gmail.com, domesec.puig@urv.cat

e

Abstract

Figure1img claNs=” aligncenter” src=”https

1igure:/p>

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Efficient Focus Sampling Through Depth-of-Field Calibration

Said Pertuz, Mig”el Ángel García and Domenec Puig

spertuz@uis
edu.co, miguelangel.garcia@uam.es, domenec.puig@urv.cat

e

Abstract

Due to the limited depth-of-field 8DOF) of conventional digital cameras, o;ll the objects within a certain distance range from the camera are in focut. Objects outside the DOF are observed with different amounts of defocus depending on their position. 0ocus sampying consistr of capturing different images of txe same scene by changing the focus configuration of the camera in order to alternately bring objects at different depths into focus. Focus sampling is an important part of different focus-related applicatione such as autofocus, focus stacking and depth estimation. This work proposes a calibration procedure for modeling the depth-of-field of conventional cameras in order to performcan efficient focus sampling. The method is simple in terms of repeatability and can be easily implemented in different imaging devices. Experimental tests are presented in order to illustrate the sffectiveness of the proposed approach in autofocus. Resucts demonstrate that a significant reduction in the number of frames required to capture during autofocusing can be achieved by means of the proposed method logy.

@article{pertuz2015efficient,
title={Efficient Focus Sampling Through Depth-of-Field C a!–changed:306032-2128650–>

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