@proceedings {990,
	title = {Assessing the variability of brain diffusion MRI preprocessing pipelines using a Region-of-Interest analysis},
	volume = {5015},
	year = {2023},
	month = {2023},
	abstract = {<p>The lack of a standardized preprocessing pipeline is a significant source of variability that might lower the reproducibility of studies, especially across sites and with incomplete description of the preprocessing workflows. We evaluate the downstream impact of variability in preprocessing workflow by quantifying the reproducibility and variability of region-of-interest (ROI) analyses. While many pipelines achieve excellent reproducibility in most ROI, we observed a large variability in performance of preprocessing workflows to the extent that some pipelines are detrimental to the data quality and reproducibility.</p>
},
	author = {Veraart, Jelle and Winzeck, Stephan and {\'A}lvaro Planchuelo-G{\'o}mez and Fricke, Bj{\"o}rn and Kornaropoulos, Evgenios N and Merisaari, Harri and Pieciak, Tomasz and Zou, Yukai and Descoteaux, Maxime}
}
@article {868,
	title = {Automatic non-destructive video estimation of maturation levels in Fuji apple (Malus Malus pumila) fruit in orchard based on colour (Vis) and spectral (NIR) data},
	journal = {Biosystems Engineering},
	volume = {195},
	year = {2020},
	pages = {136{\textendash}151},
	abstract = {Non-destructive estimates information on the desired properties of fruit without damaging them. The objective of this work is to present an algorithm for the automatic and non-destructive estimation of four maturity stages (unripe, half-ripe, ripe, or overripe) of Fuji apples (Malus Malus pumila) using both colour and spectral data from fruit. In order to extract spectral and colour data to train a proposed system, 170 samples of Fuji apples were collected. Colour and spectral features were extracted using a CR-400 Chroma Meter colorimeter and a custom set up. The second component  of  colour space and near infrared (NIR) spectrum data in wavelength ranges of 535{\textendash}560 nm, 835{\textendash}855 nm, and 950{\textendash}975 nm, were used to train the proposed algorithm. A hybrid artificial neural network-simulated annealing algorithm (ANN-SA) was used for classification purposes. A total of 1000 iterations were conducted to evaluate the reliability of the classification process. Results demonstrated that after training the correction classification rate (CCR, accuracy) was, at the best state, 100\% (test set) using both colour and spectral data. The CCR of the four different classifiers were 93.27\%, 99.62\%, 98.55\%, and 99.59\%, for colour features, spectral data wavelength ranges of 535{\textendash}560 nm, 835{\textendash}855 nm, and 950{\textendash}975 nm, respectively, over the test set. These results suggest that the proposed method is capable of the non-destructive estimation of different maturity stages of Fuji apple with a remarkable accuracy, in particular within the 535{\textendash}560 nm wavelength range.},
	doi = {https://doi.org/10.1016/j.biosystemseng.2020.04.015},
	url = {https://www.sciencedirect.com/science/article/pii/S1537511020301148},
	author = {Pourdarbani, Razieh and Sabzi, Sajad and Kalantari, Davood and Karimzadeh, Rouhollah and Ilbeygi, Elham and J I Arribas}
}
@article {864,
	title = {A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties},
	journal = {Foods},
	volume = {9},
	year = {2020},
	pages = {113},
	abstract = {Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert{\textquoteright}s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90{\textdegree}, standard deviation of GLCM matrix at 0{\textdegree}, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 {\textpm} 0.75\% over the test set, after averaging 1000 random iterations.},
	doi = {https://doi.org/10.3390/foods9020113},
	url = {https://www.mdpi.com/2304-8158/9/2/113},
	author = {Pourdarbani, Razieh and Sabzi, Sajad and Kalantari, Davood and Hern{\'a}ndez-Hern{\'a}ndez, Jos{\'e} Luis and J I Arribas}
}
@article {869,
	title = {Non-destructive Estimation of Chlorophyll a Content in Red Delicious Apple Cultivar Based on Spectral and Color Data},
	journal = {Journal of Agricultural Sciences},
	volume = {26},
	year = {2020},
	pages = {339{\textendash}348},
	abstract = {Non-destructive estimation of the chemical properties of fruit is an important goal of researchers in the food industry, since online operations, such as fruit packaging based on the amount of different chemical properties and determining different stages of handling, are done based on these estimations. In this study, chlorophyll a content in Red Delicious apple cultivar is predicted as a chemical property that is altered by apple ripening stage, using non-destructive spectral and color methods combined. Two artificial intelligence methods based on hybrid Multilayer Perceptron Neural Network - Artificial Bee Colony Algorithm (ANN-ABC) and Partial least squares regression (PLSR) were used in order to obtain a non-destructive estimation of chlorophyll a content. In application of the PLSR method, various pre-processing algorithms were used. In order to statistically properly validate the hybrid ANN-ABC predictive method, 20 runs were performed. Results showed that the best regression coefficient of the PLSR method in predicting chlorophyll a content using spectral data alone was 0.918. At the same time, the average determination coefficient over 20 repetitions in hybrid ANN-ABC in the estimation of chlorophyll a content, using spectral data and color features were higher than 0.92{\textpm}0.040 and 0.89{\textpm}0.045, respectively, which to our knowledge is a remarkable non-intrusive estimation result.},
	doi = {https://doi.org/10.15832/ankutbd.523574},
	url = {https://dergipark.org.tr/en/pub/ankutbd/issue/56429/523574},
	author = {Yousef Abbaspour-Gilandeh and Sabzi, Sajad and Azadshahraki, Farzad and Karimzadeh, Rouhollah and Ilbeygi, Elham and J I Arribas}
}
@article {870,
	title = {Non-destructive visible and short-wave near-infrared spectroscopic data estimation of various physicochemical properties of Fuji apple (Malus pumila) fruits at different maturation stages},
	journal = {Chemometrics and Intelligent Laboratory Systems},
	year = {2020},
	pages = {104147},
	abstract = {measurement of physicochemical properties of fruits during maturation stages can help having proper fruit management. Spectroscopy data analyzing and processing is among the commonly used methods that enable non-destructive accurate property estimation. Non-destructive linear (partial least squares regression, PSLR) and non-linear (artificial neural network, ANN) regression estimation of different physicochemical properties including firmness, acidity (pH) and starch content of 160 Fuji (Malus pumila) apple fruit samples at various maturity stages using visible and short wave near infrared (VSWIR) spectroscopic data in wavelength range 400{\textendash}1000 nm is investigated with the following steps: (1) harvesting 160 Fuji apple samples at four different maturation levels; (2) extracting spectral data in wavelength range of 400{\textendash}1000 nm; extracting physicochemical properties of tissue firmness, acidity (pH) and starch content; (3) pre-processing the reflectance spectra from each sample; (4) selecting effective wavelength values for each chemical property; and (5) non-destructive estimation of tissue firmness, acidity (pH) and starch content using spectral data range 400{\textendash}1000 nm and spectral data based on effective wavelengths, by means of an ensemble average artificial neural network method. Results show that the neural ensemble reached similar results when using VSWIR spectral data content (wavelength range) and fixed effective selected NIR wavelengths. Correlation coefficients estimating tissue firmness, acidity (pH), and starch content were 0.800, 0.919, and 0.940 for VSWIR spectral data (linear PLS regression), 0.826, 0.947, and 0.969 for VSWIR spectral data (non-linear ANN), 0.827, 0.946, and 0.969 for fixed NIR effective wavelengths (non-linear ANN). Mean {\textpm} std. Regression coefficients for tissue firmness, acidity (pH), and starch content were 0.717 {\textpm} 0.113, 0.786 {\textpm} 0.131, and 0.941 {\textpm} 0.013 for Vis/NIR spectral data (linear PLS regression), 0.849 {\textpm} 0.017, 0.930 {\textpm} 0.017, and 0.967 {\textpm} 0.007 for Vis/NIR spectral data (non-linear ANN), 0.852 {\textpm} 0.016, 0.929 {\textpm} 0.015, and 0.966 {\textpm} 0.006 for fixed effective NIR wavelengths (non-linear ANN).},
	doi = {https://doi.org/10.1016/j.chemolab.2020.104147},
	url = {https://www.sciencedirect.com/science/article/pii/S016974392030304X},
	author = {Pourdarbani, Razieh and Sabzi, Sajad and Kalantari, Davood and J I Arribas}
}
@article {768,
	title = {A novel infrared video surveillance system using deep learning based techniques},
	journal = {Multimedia Tools and Applications},
	volume = {77},
	year = {2018},
	chapter = {26657},
	author = {Zhang, Huaizhong and Luo, Chunbo and Wang, Qi and Kitchin, Matthew and Parmley, Andrew and Monge-Alvarez, Jesus and Pablo Casaseca-de-la-Higuera}
}
@article {654,
	title = {Spatially-variant noise filtering in Magnetic Resonance Imaging: A Consensus-based approach},
	journal = {Knowledge-Based Systems},
	year = {2016},
	month = {2016},
	doi = {http://dx.doi.org/10.1016/j.knosys.2016.05.053},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705116301575},
	author = {Luis Gonz{\'a}lez-Jaime and Gonzalo Vegas-S{\'a}nchez-Ferrero and Etienne E. Kerre and Santiago Aja-Fern{\'a}ndez}
}
@conference {774,
	title = {Systematic infrared image quality improvement using deep learning based techniques},
	booktitle = {Remote Sensing Technologies and Applications in Urban Environments},
	year = {2016},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	author = {Zhang, Huaizhong and Pablo Casaseca-de-la-Higuera and Luo, Chunbo and Wang, Qi and Kitchin, Matthew and Parmley, Andrew and Monge-Alvarez, Jesus}
}
@conference {gonzalez2013applying,
	title = {Applying a parametric approach for the task of nonstationary noise removal with missing information},
	booktitle = {Computational Cybernetics (ICCC), 2013 IEEE 9th International Conference on},
	year = {2013},
	pages = {23{\textendash}28},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Luis Gonz{\'a}lez-Jaime and Nachtegeal, Mike and Kerre, Etienne and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez}
}
@inbook {gonzalez2013parametric,
	title = {Parametric Image Restoration Using Consensus: An Application to Nonstationary Noise Filtering},
	booktitle = {Pattern Recognition and Image Analysis},
	year = {2013},
	pages = {358{\textendash}365},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	author = {Luis Gonz{\'a}lez-Jaime and Nachtegeal, Mike and Kerre, Etienne and Gonzalo Vegas-S{\'a}nchez-Ferrero and Santiago Aja-Fern{\'a}ndez}
}
@conference {478,
	title = {Automatic segmentation of white matter structures from DTI using tensor invariants and tensor orientation},
	booktitle = {Proc. Intl. Soc. Mag. Reson. Med},
	year = {2009},
	author = {Rodrigo de Luis-Garc{\'\i}a and Lopez, C Alberola and Kindlmann, G and Carl-Fredik Westin}
}
@article {krissian2009noise,
	title = {Noise-driven anisotropic diffusion filtering of MRI},
	journal = {Image Processing, IEEE Transactions on},
	volume = {18},
	number = {10},
	year = {2009},
	pages = {2265{\textendash}2274},
	publisher = {IEEE},
	author = {K Krissian and Santiago Aja-Fern{\'a}ndez}
}
@inbook {brun2009similar,
	title = {Similar Tensor Arrays{\textendash}A Framework for Storage of Tensor Array Data},
	booktitle = {Tensors in Image Processing and Computer Vision},
	year = {2009},
	pages = {407{\textendash}428},
	publisher = {Springer London},
	organization = {Springer London},
	author = {Brun, Anders and Marcos Martin-Fernandez and Acar, Burak and Emma Mu{\~n}oz-Moreno and Cammoun, Leila and Sigfridsson, Andreas and Dario Sosa-Cabrera and Svensson, Bj{\"o}rn and Herberthson, Magnus and Knutsson, Hans}
}
@article {aja2008restoration,
	title = {Restoration of DWI data using a Rician LMMSE estimator},
	journal = {Medical Imaging, IEEE Transactions on},
	volume = {27},
	number = {10},
	year = {2008},
	pages = {1389{\textendash}1403},
	publisher = {IEEE},
	author = {Santiago Aja-Fern{\'a}ndez and Niethammer, Marc and Kubicki, Marek and Martha E Shenton and Carl-Fredik Westin}
}
@proceedings {aja2008unbiased,
	title = {An unbiased Non-Local Means scheme for DWI filtering},
	year = {2008},
	pages = {277{\textendash}284},
	author = {Santiago Aja-Fern{\'a}ndez and K Krissian}
}
@article {bouix2007evaluating,
	title = {On evaluating brain tissue classifiers without a ground truth},
	journal = {Neuroimage},
	volume = {36},
	number = {4},
	year = {2007},
	pages = {1207{\textendash}1224},
	publisher = {Academic Press},
	author = {Bouix, Sylvain and Marcos Martin-Fernandez and Ungar, Lida and Nakamura, Motoaki and Koo, Min-Seong and McCarley, Robert W and Martha E Shenton}
}
@inbook {westin2006tensor,
	title = {Tensor field regularization using normalized convolution and markov random fields in a bayesian framework},
	booktitle = {Visualization and Processing of Tensor Fields},
	year = {2006},
	pages = {381{\textendash}398},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	author = {Carl-Fredik Westin and Marcos Martin-Fernandez and Carlos Alberola-Lopez and Juan Ruiz-Alzola and Knutsson, Hans}
}
@inbook {jose2003lncs,
	title = {Freehand Ultrasound Reconstruction Based on ROI Prior Modeling and Normalized Convolution},
	booktitle = {Lecture Notes in Computer Science},
	volume = {2879},
	year = {2003},
	pages = {382{\textendash}390},
	publisher = {Berlin: Springer-Verlag, 1973-},
	organization = {Berlin: Springer-Verlag, 1973-},
	author = {Raul San Jose-Estepar and Marcos Martin-Fernandez and Carlos Alberola-Lopez and Ellsmere, James and Kikinis, Ron and Carl-Fredik Westin}
}
@inbook {jose2003freehand,
	title = {Freehand Ultrasound Reconstruction Based on ROI Prior Modeling and Normalized Convolution},
	booktitle = {Lecture Notes in Computer Science},
	volume = {2879},
	year = {2003},
	pages = {382{\textendash}390},
	publisher = {Berlin: Springer-Verlag, 1973-},
	organization = {Berlin: Springer-Verlag, 1973-},
	author = {Raul San Jose-Estepar and Marcos Martin-Fernandez and Carlos Alberola-Lopez and Ellsmere, James and Kikinis, Ron and Carl-Fredik Westin}
}
