PUBLICATIONS

– Bento, M., Fantini, I., Park, J., Rittner, L., & Frayne, R. (2022). Deep learning in large and multi-site structural brain MR imaging datasets. Frontiers in Neuroinformatics, 15, 805669.

– Frittoli, R., Pereira, D., Lapa, A., Postal, M., Sinicato, N., Fernandes, P., Cendes, F., Castellano, G., Rittner, L., Marini, R., & others (2022). Axonal dysfunction is associated with interferon-$γ$ levels in childhood-onset systemic lupus erythematosus: a multivoxel magnetic resonance spectroscopy study. Rheumatology, 61(4), 1529–1537.

– Pereira, D., Freschi, M., Frittoli, R., Londe, A., Amaral, T., Dertkigil, S., Del Rio, A., Cendes, F., Rittner, L., & Appenzeller, S. (2021). Hippocampal subfields volume reduction in patients with systemic sclerosis: a longitudinal magnetic resonance imaging (MRI) volumetric study. In 

– Cruciani, F., Brusini, L., Zucchelli, M., Retuci Pinheiro, G., Setti, F., Boscolo Galazzo, I., Deriche, R., Rittner, L., Calabrese, M., & Menegaz, G. (2021). Explainable 3D-CNN for multiple sclerosis patients stratification. In International Conference on Pattern Recognition (pp. 103–114).

– Cruciani, F., Brusini, L., Zucchelli, M., Pinheiro, G., Setti, F., Galazzo, I., Deriche, R., Rittner, L., Calabrese, M., & Menegaz, G. (2021). Interpretable deep learning as a means for decrypting disease signature in multiple sclerosis. Journal of Neural Engineering, 18(4), 0460a6.

– Romero, E., Costa, E., Brieva, J., Rittner, L., Linguraru, M., & Lepore, N. (2021). 17th International Symposium on Medical Information Processing and Analysis. In Symposium on Medical Information Processing and Analysis (pp. 10).

– Carmo, D., Campiotti, I., Fantini, I., Rodrigues, L., Rittner, L., & Lotufo, R. (2021). Multitasking segmentation of lung and COVID-19 findings in CT scans using modified EfficientDet, UNet and MobileNetV3 models. In 17th International Symposium on Medical Information Processing and Analysis (pp. 65–74).

– Rodrigues, J., Pinheiro, G., Carmo, D., & Rittner, L. (2021). Volumetric segmentation of the corpus callosum: training a deep learning model on diffusion MRI. In 17th International Symposium on Medical Information Processing and Analysis (pp. 198–207).

– Carmo, D., Campiotti, I., Rodrigues, L., Fantini, I., Pinheiro, G., Moraes, D., Nogueira, R., Rittner, L., & Lotufo, R. (2021). Rapidly deploying a COVID-19 decision support system in one of the largest Brazilian hospitals. Health Informatics Journal, 27(3), 14604582211033017.

– Caldeira, T., Julio, P., Appenzeller, S., & Rittner, L. (2021). insight: A software for exploration and visualization of DT-MRI data of the Corpus Callosum. Computers & Graphics, 99, 259–271.

– Fantini, I., Yasuda, C., Bento, M., Rittner, L., Cendes, F., & Lotufo, R. (2021). Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging. Computerized Medical Imaging and Graphics, 90, 101897.

– Pinheiro, G., Brusini, L., Bajrami, A., Pizzini, F., Calabrese, M., Reis, F., Appenzeller, S., Menegaz, G., & Rittner, L. (2021). Diffusion MRI and silver standard masks to improve CNN-based thalamus segmentation. In Medical Imaging 2021: Image Processing (pp. 692–698).

– Pereira, D., Ganaha, L., Appenzeller, S., & Rittner, L. (2021). Open-source toolbox for analysis and spectra quality control of magnetic resonance spectroscopic imaging. In Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging (pp. 64–71).

– Carmo, D., Silva, B., Yasuda, C., Rittner, L., Lotufo, R., Alzheimer’s Disease Neuroimaging Initiative, & others (2021). Hippocampus segmentation on epilepsy and Alzheimer’s disease studies with multiple convolutional neural networks. Heliyon, 7(2), e06226.

– Carmo, D., Rittner, L., & Lotufo, R. (2020). MultiATTUNet: brain tumor segmentation and survival multitasking. In International MICCAI Brainlesion Workshop (pp. 424–434).

– Frittoli, R., Rodrigues, D., Lapa, A., Postal, M., Marini, R., Castellano, G., Cendes, F., Rittner, L., & Appenzeller, S. (2020). Longitudinal evaluation of axonal dysfunction, neuronal markers and serum cytokines in childhood-onset systemic lupus erythematosus. In Arthritis & Rheumatology.

– Julio, P., Frittoli, R., Lapa, A., Caldeira, T., Rittner, L., Cendes, F., Marini, R., Fernandes, P., Costallat, L., & Appenzeller, S. (2020). Microstructural Damage Is Associated with Age at Disease-onset and Cognitive Impairment in Systemic Lupus Erythematosus. In ARTHRITIS & RHEUMATOLOGY.

– Pinheiro, G., Carmo, D., Yasuda, C., Lotufo, R., & Rittner, L. (2020). Convolutional Neural Network on DTI Data for Sub-cortical Brain Structure Segmentation. In Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019 (pp. 135–146).

– Frittoli, R., Pereira, D., Rittner, L., & Appenzeller, S. (2020). Proton magnetic resonance spectroscopy (1H-MRS) in rheumatic autoimmune diseases: A systematic review. Lupus, 29(14), 1873–1884.

– Pereira, M., Fantini, I., Lotufo, R., & Rittner, L. (2020). An extended-2D CNN for multiclass Alzheimer’s Disease diagnosis through structural MRI. In Medical Imaging 2020: Computer-Aided Diagnosis (pp. 438–444).

– Herrera, W., Pereira, M., Bento, M., Lapa, A., Appenzeller, S., & Rittner, L. (2020). A framework for quality control of corpus callosum segmentation in large-scale studies. Journal of Neuroscience Methods, 334, 108593.

– Rodrigues, L., Rezende, T., Zanesco, A., Hernandez, A., Franca, M., & Rittner, L. (2020). Hypothalamus fully automatic segmentation from MR images using a U-Net based architecture. In 15th International Symposium on Medical Information Processing and Analysis (pp. 144–150).

– Souza, R., Lucena, O., Bento, M., Garrafa, J., Rittner, L., Appenzeller, S., Lotufo, R., & Frayne, R. (2019). Brain Extraction Network Trained with” Silver Standard” Data and Fine-Tuned with Manual Annotation for Improved Segmentation. In 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (pp. 234–240).

– Souza Castro, J., Rittner, L., Appenzeller, S., Yamaguti-Hayakawa, G., Colella, M., De Paula, E., Annichino-Bizzacchi, J., Reis, F., & Orsi, F. (2019). Association of plasmic score with neurological symptoms in patients with thrombotic thrombocytopenic purpura (TTP). Blood, 134, 4894.

– Lucena, O., Souza, R., Rittner, L., Frayne, R., & Lotufo, R. (2019). Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks. Artificial Intelligence in Medicine, 98, 48–58.

– Bento, M., Souza, R., Salluzzi, M., Rittner, L., Zhang, Y., & Frayne, R. (2019). Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set. Magnetic resonance imaging, 62, 18–27.

– Pereira, D., Freschi, M., Frittoli, R., Amaral, T., Dertkigil, S., Rio, A., Castellano, G., Cendes, F., Rittner, L., & Appenzeller, S.. (2019). THU0324 AXONAL DYSFUNCTION IN CEREBRAL WHITE MATTER IN SYSTEMIC SCLEROSIS: A PROTON MAGNETIC RESONANCE SPECTROSCOPIC IMAGING ($^1$H-MRSI) STUDY.

– Herrera, W., Bento, M., & Rittner, L. (2019). Corpus Callosum Shape Signature for Segmentation Evaluation. In XXVI Brazilian Congress on Biomedical Engineering: CBEB 2018, Arma\cc\~ao de Buzios, RJ, Brazil, 21-25 October 2018 (Vol. 2) (pp. 143–147).

– Carmo, D., Silva, B., Yasuda, C., Rittner, L., & Lotufo, R. (2019). Extended 2D Consensus Hippocampus Segmentation. arXiv preprint arXiv:1902.04487.

– Kuijf, H., Biesbroek, J., Bresser, J., Heinen, R., Andermatt, S., Bento, M., Berseth, M., Belyaev, M., Cardoso, M., Casamitjana, A., & others (2019). Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge. IEEE transactions on medical imaging.

– Fontolan, J., Pereira, D., Souza, R., Appenzeller, S., & Rittner, L. (2019). Improving estimates of brain metabolite concentrations in MR spectroscopic imaging (MRSI) through MRI content. In Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging (pp. 109530V).

– Pinheiro, G., Voltoline, R., Bento, M., & Rittner, L. (2019). V-net and u-net for ischemic stroke lesion segmentation in a small dataset of perfusion data. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4 (pp. 301–309).

– Pinto, W., Tamires, A., Pereira, D., Rittner, L., & Appenzeller, S. (2018). Thalamic Atrophy and Mood Changes in juvenile Systemic Erythematosus Lupus (SEL).. Revista dos Trabalhos de Iniciação Científica da UNICAMP(26).

– Horvath, J., Pereira, D., Rittner, L., Appenzeller, S., & Castellano, G. (2018). Study of the technique of magnetic resonance spectroscopic imaging (MRSI) and application to evaluation of brain metabolites of systemic lupus erythematosus patients. Revista dos Trabalhos de Inicia\cc\~ao Cient\’\ifica da UNICAMP(26).

– Rodrigues, L., Souza, R., Rittner, L., Frayne, R., & Lotufo, R. (2018). Common Carotid Artery Lumen Automatic Segmentation from Cine Fast Spin Echo Magnetic Resonance Imaging. In Sipaim–Miccai Biomedical Workshop (pp. 16–24).

– Ishii, F., Flores, F., & Rittner, L. (2018). Tensorial Lucas-Kanade: An Optical Flow Estimator Based on Tensorial Color Representation and Tensorial Algebra. In 2018 IEEE Symposium on Computers and Communications (ISCC) (pp. 00633–00639).

– Fantini, I., Rittner, L., Yasuda, C., & Lotufo, R. (2018). Automatic detection of motion artifacts on MRI using Deep CNN. In 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI) (pp. 1–4).

– Souza, R., Lucena, O., Bento, M., Garrafa, J., Appenzeller, S., Rittner, L., Lotufo, R., & Frayne, R. (2018). Reliability of using single specialist annotation for designing and evaluating automatic segmentation methods: A skull stripping case study. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 1344–1347).

– Pereira, M., Cover, G., Appenzeller, S., & Rittner, L. (2018). Corpus callosum parcellation methods: a quantitative comparative study. In Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging (pp. 271–276).

– Pinheiro, G., Cover, G., Bento, M., & Rittner, L. (2018). Automatic callosal fiber convergence plane computation through DTI-based divergence map. In Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging (pp. 256–264).

– Bento, M., Souza, R., Lotufo, R., Frayne, R., & Rittner, L. (2018). WMH segmentation challenge: a texture-based classification approach. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers 3 (pp. 489–500).

– Bombini, M., Peres, F., Lapa, A., Sinicato, N., Quental, B., Pincelli, ., Amaral, T., Gomes, C., Rio, A., Marques-Neto, J., & others (2018). Olfactory function in systemic lupus erythematosus and systemic sclerosis. A longitudinal study and review of the literature. Autoimmunity reviews, 17(4), 405–412.

– Costallat, B., Ferreira, D., Lapa, A., Rittner, L., Costallat, L., & Appenzeller, S. (2018). Brain diffusion tensor MRI in systematic lupus erythematosus: A systematic review. Autoimmunity reviews, 17(1), 36–43.

– Cover, G., Herrera, W., Bento, M., Appenzeller, S., & Rittner, L. (2018). Computational methods for corpus callosum segmentation on MRI: A systematic literature review. Computer Methods and Programs in Biomedicine, 154, 25–35.

– Lucena, O., Souza, R., Rittner, L., Frayne, R., & Lotufo, R. (2018). Silver standard masks for data augmentation applied to deep-learning-based skull-stripping. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 1114–1117).

– Herrera, W., Cover, G., & Rittner, L. (2018). Pixel-based classification method for corpus callosum segmentation on diffusion-MRI. In VipIMAGE 2017: Proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017 (pp. 217–224).

– Cover, G., Herrera, W., Bento, M., & Rittner, L. (2018). Corpus callosum 2d segmentation on diffusion tensor imaging using growing neural gas network. In VipIMAGE 2017: Proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017 (pp. 208–216).

– Souza, R., Lucena, O., Garrafa, J., Gobbi, D., Saluzzi, M., Appenzeller, S., Rittner, L., Frayne, R., & Lotufo, R. (2018). An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement. NeuroImage, 170, 482–494.

– Bento, M., Salluzzi, M., Gobbi, D., Frayne, R., Rittner, L., & others (2017). Characterizing white matter hyperintensities for longitudinal atherosclerosis studies. International journal of stroke.

– Rodrigues, L., Souza, R., Rittner, L., Lotufo, R., Frayne, R., & others (2017). Semi-automatic common carotid lumen segmentation on dynamic mr images. International journal of stroke.

– Rodrigues, L., De Souza, R., Rittner, L., Frayne, R., & Lotufo, R. (2017). Common Carotid Artery Lumen Segmentation from Cardiac Cycle-resolved Cine Fast Spin Echo Magnetic Resonance Imaging. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (pp. 442–449).

– Cover, G., Pereira, M., Bento, M., Appenzeller, S., & Rittner, L. (2017). Data-Driven Corpus Callosum Parcellation Method Through Diffusion Tensor Imaging. IEEE Access, 5, 22421–22432.

– Domingues, J., Tavares, P., Souza, L., Rezende, T., Casseb, R., Martinez, A., Rittner, L., Appenzeller, S., Nucci, A., & Fran\cca, M. (2017). Predominant posterior cerebral cortical atrophy in patients with DMD mutations. Neuromuscular Disorders, 27, S115.

– Bento, M., Sym, Y., Frayne, R., Lotufo, R., & Rittner, L. (2017). Probabilistic segmentation of brain white matter lesions using texture-based classification. In Image Analysis and Recognition: 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017, Proceedings 14 (pp. 71–78).

– Sinicato, N., Postal, M., Oliveira Peliccari, K., Rittner, L., Marini, R., & Appenzeller, S. (2017). Prevalence and features of metabolic syndrome in childhood-onset systemic lupus erythematosus. Clinical rheumatology, 36(7), 1527–1535.

– Postal, M., Lapa, A., Reis, F., Rittner, L., & Appenzeller, S. (2017). Magnetic resonance imaging in neuropsychiatric systemic lupus erythematosus: current state of the art and novel approaches. Lupus, 26(5), 517–521.

– Souza, R., Rittner, L., Machado, R., & Lotufo, R. (2017). iamxt: Max-tree toolbox for image processing and analysis. SoftwareX, 6, 81–84.

– SOUZA, R., TAVARES, L., RITTNER, L., & LOTUFO, R. (2016). Hands-on Morphological Processing using the Max-tree Data Structure. 

– Peres, F., Pelicari, K., Postal, M., Sinicato, N., Rittner, L., Costallat, L., & Appenzeller, S. (2016). Alterations in Sense of Smell and Limbic Structures in Patients with Systemic Lupus Erythematosus during 3-Years Follow-up. In ARTHRITIS & RHEUMATOLOGY.

– Bombini, M., Peres, F., Sinicato, N., Lapa, A., Rittner, L., Souza, R., Rio, A., Marques-Neto, J., & Appenzeller, S. (2016). Olfactory Impairment Is Associate with Cognitive Dysfunction and Regional Brain Atrophy in Systemic Sclerosis. In ARTHRITIS & RHEUMATOLOGY.

– Colella, S., Appenzeller, S., Dertkigil, S., & Rittner, L. (2016). Método automático de segmentação dos pulmões em imagens de CT baseado na max-tree. In Proceedings of XXV Congresso Brasileiro de Engenharia Biomédica.

– Tavares, L., Souza, R., Rittner, L., Machado, R., & Lotufo, R. (2016). A max-tree simplification proposal and applications for the interactive max-tree visualization tool. In 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (pp. 313–320).

– Ghamisi, P., Souza, R., Benediktsson, J., Zhu, X., Rittner, L., & Lotufo, R. (2016). Extended extinction profile for the classification of hyperspectral images. In 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (pp. 1–4).

– Souza, R., Tavares, L., Rittner, L., & Lotufo, R. (2016). An overview of max-tree principles, algorithms and applications. In 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T) (pp. 15–23).

– Pinheiro, G., Soares, G., Costa, A., Lotufo, R., & Rittner, L. (2016). Divergence map from diffusion tensor imaging: Concepts and application to corpus callosum. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1120–1123).

– Ghamisi, P., Souza, R., Benediktsson, J., Rittner, L., Lotufo, R., & Zhu, X. (2016). Hyperspectral data classification using extended extinction profiles. IEEE Geoscience and Remote Sensing Letters, 13(11), 1641–1645.

– Ghamisi, P., Souza, R., Benediktsson, J., Zhu, X., Rittner, L., & Lotufo, R. (2016). Extinction profiles for the classification of remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 5631–5645.

– Ghamisi, P., Souza, R., Rittner, L., Benediktsson, J., Lotufo, R., & Zhu, X. (2016). Extinction profiles: A novel approach for the analysis of remote sensing data. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5122–5125).

– Leite, M., Gobbi, D., Salluzi, M., Frayne, R., Lotufo, R., & Rittner, L. (2016). 3D texture-based classification applied on brain white matter lesions on MR images. In Medical Imaging 2016: Computer-Aided Diagnosis (pp. 97852N).

– Tamires Lapa, A., Postal, M., Angélica Sinicato, N., Geraldo Ferreira, W., Siqueira Bellini, B., Teixeira Fernandes, P., Rittner, L., Marini, R., Cendes, F., & Appenzeller, S. (2016). Reduction of Cerebral and Corpus Callosum Volumes in Childhood-Onset Systemic Lupus Erythematosus: A Volumetric Magnetic Resonance Imaging Analysis. Arthritis & Rheumatology, 68(9), 2193–2199.

– Costa, A., Rittner, L., Lotufo, R., & Appenzeller, S. (2015). Mid-callosal plane determination using preferred directions from diffusion tensor images. In Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging (pp. 615–623).

– Leite, M., Rittner, L., Appenzeller, S., Ruocco, H., & Lotufo, R. (2015). Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging. Journal of Medical Imaging, 2(1), 014002.

– Frittoli, R., Postal, M., Pelicari, K., Sinicato, N., Lapa, A., Peres, F., Cendes, F., Marini, R., Castellano, G., Rittner, L., & others (2015). Axonal Dysfunction in Childhood-onset Systemic Lupus Erythematosus. Association with Neuropsychiatric Manifestations and Disease Activity. Arthritis & Rheumatology, 67, 2429–2430.

– Tavares, L., Souza, R., Rittner, L., Machado, R., & Lotufo, R. (2015). Interactive max-tree visualization tool for image processing and analysis. In 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 119–124).

– Souza, R., Rittner, L., Lotufo, R., & Machado, R. (2015). An array-based node-oriented max-tree representation. In 2015 IEEE International Conference on Image Processing (ICIP) (pp. 3620–3624).

– Lapa, A., Barbosa, R., Mendon\cca, I., Souza, M., Peres, F., Marini, R., Rittner, L., Fran\cca, M., Bergo, F., Cendes, F., & others. (2015). AB1021 Prevalence and Neuroimaging Correlates of Central Ataxia In Childhood-Onset Systemic Lupus Erythematosus.

– Testoni, V., Penatti, O., Andal\’o, F., Lizarraga, M., Rittner, L., Valle, E., & Avila, S. (2015). Guest Editorial: Special Issue on Vision-based Human Activity Recognition. Journal of Communication and Information Systems, 30(1).

– Souza, R., Rittner, L., Machado, R., & Lotufo, R. (2015). A comparison between extinction filters and attribute filters. In Mathematical Morphology and Its Applications to Signal and Image Processing: 12th International Symposium, ISMM 2015, Reykjavik, Iceland, May 27-29, 2015. Proceedings 12 (pp. 63–74).

– Rittner, L., Bento, M., Costa, A., Souza, R., Machado, R., & Lotufo, R. (2015). Web-based platform for collaborative medical imaging research. In Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations (pp. 54–61).

– Souza, R., Rittner, L., Machado, R., & Lotufo, R. (2014). Maximal max-tree simplification. In 2014 22nd International Conference on Pattern Recognition (pp. 3132–3137).

– Rittner, L., Freitas, P., Appenzeller, S., & Lotufo, R. (2014). Automatic DTI-based parcellation of the corpus callosum through the watershed transform. Revista Brasileira de Engenharia Biomédica, 30, 132–143.

– Rittner, L., Udupa, J., & Torigian, D. (2014). Multiple fuzzy object modeling improves sensitivity in automatic anatomy recognition. In Medical Imaging 2014: Image Processing (pp. 723–729).

– Souza, R., Rittner, L., & Lotufo, R. (2014). A comparison between k-optimum path forest and k-nearest neighbors supervised classifiers. Pattern recognition letters, 39, 2–10.

– Bento, M., Rittner, L., Lotufo, R., & Appenzeller, S. (2013). Texture descriptors and pattern recognition classifiers based analysis of white matter hyperintensity in MR images. In Proceedings of Workshop of Theses and Dissertations in SIBGRAPI.

– Lapa, A., Bento, M., Rittner, L., Ruocco, H., Castellano, G., Damasceno, B., Costallat, L., Lotufo, R., Cendes, F., & Appenzeller, S. (2013). THU0335 Support vector machines classification of texture parameters of white matter lesions in childhood-onset systemic lupus erythematosus. Possible mechanism to distinguish between demyelination and ischemia. Annals of the Rheumatic Diseases, 71(Suppl 3), 269–269.

– Lapa, A., Bento, M., Rittner, L., Ruocco, H., Castellano, G., Damasceno, B., Costallat, L., Appenzeller, S., Lotufo, R., & Cendes, F. (2013). THU0160 White matter lesions are predominantly demyelinating in systemic lupus erythematosus. An support vector machines classification of texture parameters. Annals of the Rheumatic Diseases, 71(Suppl 3), 209–210.

– Bento, M., Rittner, L., Appenzeller, S., Lapa, A., & Lotufo, R. (2013). Analysis of brain white matter hyperintensities using pattern recognition techniques. In Medical Imaging 2013: Image Processing (pp. 1021–1027).

– Rittner, L., Campbell, J., Freitas, P., Appenzeller, S., Bruce Pike, G., & Lotufo, R. (2013). Analysis of scalar maps for the segmentation of the corpus callosum in diffusion tensor fields. Journal of mathematical imaging and vision, 45(3), 214–226.

– Souza, R., Lotufo, R., Rittner, L., & others (2012). A Comparison Between Optimum-path Forest And $κ$-nearest Neighbors Classifiers. In Brazilian Symposium of Computer Graphic and Image Processing.

– Bento, M., Rittner, L., Lotufo, R., & Appenzeller, S. (2012). Analysis of Brain White Matter Hyperintensities. In Workshop of Works in Progress (WIP) in SIBGRAPI (pp. 3–4).

– Souza, R., Lotufo, R., & Rittner, L. (2012). A comparison between optimum-path forest and k-nearest neighbors classifiers. In 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images (pp. 260–267).

– Freitas, P., Rittner, L., Appenzeller, S., Lapa, A., & Lotufo, R. (2012). Watershed-based segmentation of the corpus callosum in difusion MRI. In Medical Imaging 2012: Image Processing (pp. 879–885).

– Bento, A., Rittner, M., Castellano, L., Ruocco, G., Damasceno, H., Costallat, B., & others (2011). Support Vector Machines Classification of Texture Parameters of White Matter Lesions in Systemic Lupus Erythematosus. Possible Mechanism to Distinguish Between Demyelination and Ischemia.. Arthritis & Rheumatism, 63, 2257.

– Lapa, A., Bento, M., Rittner, L., Castellano, G., Ruocco, H., Damasceno, B., Costallat, L., Lotufo, R., Cendes, F., & Appenzeller, S. (2011). Support vector machines classification of texture parameters of white matter lesions in systemic lupus erythematosus. possible mechanism to distinguish between demyelination and ischemia. In ARTHRITIS AND RHEUMATISM (pp. S883–S883).

– Machado, R., Rittner, L., & Lotufo, R. (2011). Adessowiki-Collaborative platform for writing executable papers. Procedia Computer Science, 4, 759–767.

– Rittner, L., Saude, A., Silva, A., Machado, R., Bento, M., & Lotufo, R. (2011). Adessowiki: Collaborative Scientific Programming Environment. In 2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials (pp. 56–62).

– Lotufo, R., Rittner, L., Audigier, R., Machado, R., & Sa\’ude, A. (2011). Morphological Image Processing Applied in Biomedicine. Biomedical Image Processing, 107–129.

– Freitas, P., Rittner, L., Appenzeller, S., & Lotufo, R. (2011). Watershed-based segmentation of the midsagittal section of the corpus callosum in diffusion MRI. In 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images (pp. 274–280).

– Rittner, L., & Lotufo, R. (2010). Processamento de imagens de tensores de difusão. Neurociências e Epilepsia, 2, 81–85.

– Rittner, L., Lotufo, R., Campbell, J., & Pike, G. (2010). Segmentation of thalamic nuclei based on tensorial morphological gradient of diffusion tensor fields. In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 1173–1176).

– Rittner, L., Flores, F., & Lotufo, R. (2010). A tensorial framework for color images. Pattern Recognition Letters, 31(4), 277–296.

– Appenzeller, S., Pike, B., Rittner, L., Leonard, G., Veilleux, M., & Clarke, A. (2009). Thalamic volumes predict cognitive impairment evaluated by speed processing tasks in systemic lupus erythematosus. Arthritis Rheum, 60(10).

– Rittner, L., & Lotufo, R. (2009). Segmentação de imagens de tensores de difusão no contexto da morfologia matemática. (Doctoral dissertation, Tese (Doutorado)—Faculdade de Engenharia Elétrica e Computação-FEEC).

– Rittner, L., Appenzeller, S., & Lotufo, R. (2009). Segmentation of brain structures by watershed transform on tensorial morphological gradient of diffusion tensor imaging. In 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing (pp. 126–132).

– Rittner, L., & Alencar Lotufo, R. (2009). Segmentation of DTI based on tensorial morphological gradient. In Medical Imaging 2009: Image Processing (pp. 477–488).

– Neto, A., Rittner, L., Zampieri, D., & Corr\^ea-Victorino, A. (2008). Nondeterministic criteria to discard redundant information in real time autonomous navigation systems based on monocular vision. In 2008 IEEE International Symposium on Intelligent Control (pp. 420–425).

– Rittner, L., & Lotufo, R. (2008). Diffusion tensor imaging segmentation by watershed transform on tensorial morphological gradient. In 2008 XXI Brazilian Symposium on Computer Graphics and Image Processing (pp. 196–203).

– Miranda Neto, A., Rittner, L., Leite, N., Zampieri, D., Lotufo, R., & Mendeleck, A. (2007). Pearson’s Correlation Coefficient for Discarding Redundant Information in Real Time Autonomous Navigation Systems. In Proceedings of the IEEE Multi-conference on Systems and Control, MSC 2007 (pp. 426–431).

– Rittner, L., Flores, F., & Lotufo, R. (2007). New tensorial representation of color images: Tensorial morphological gradient applied to color image segmentation. In XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2007) (pp. 45–52).

– Miranda Neto, A., Rittner, L., Leite, N., Zampieri, D., Lotufo, R., & Mendeleck, A. (2007). Pearson’s correlation coefficient for discarding redundant information in real time autonomous navigation system. In 2007 IEEE International Conference on Control Applications (pp. 426–431).

– Miranda Neto, A., & Rittner, L. (2006). A simple and efficient road detection algorithm for real time autonomous navigation based on monocular vision. In 2006 IEEE 3rd Latin American Robotics Symposium (pp. 92–99).

– Rittner, L. (2004). Identificaçao e Transformação de Valores Aberrantes como Medida de confiabilidade do Método das Diferen\ccas para Estimativa de Fluxo \’Optico em Sequencias de Imagens. (Doctoral dissertation, Tese (Mestrado em Engenharia Elétrica)–Faculdade de Engenharia Elétrica e~…).