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Intravital Microscopy
Intravital Microscopy

Intravital microscopy setup

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Leukocyte recruitment

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About us

Welcome to the Biomedical Image Processing (BIP) Group.


Founded in 2011 by professor Ricardo J. Ferrari, Ph.D. with the goal of developing practical and efficient solutions to significant problems in the biomedical field, the BIPGroup has a focus on Image and Video Processing, Computer Vision, and Machine Learning (including CNNs, Autoencoders, ...) techniques for automatically interpreting biomedical images.

Current research projects aim to segmenting, classifying and assessing 3D magnetic resonance (MR) brain images for neurodegenerative diseases' studies, such as Alzheimer's and Multiple Sclerosis, among others medical imaging issues.

Our group is composed of innovative, talented and committed under and graduate students and, despite being relatively small, it is vibrant and very focused. 

If you would like to know more about our researchers or be part of our group, please contact us. We are always looking for very interested and committed students to join our team.

Companies interested in consulting services or collaboration in research projects may also contact Prof. Dr. Ricardo J. Ferrari.

  • Biomedical Image / Video Processing

  • Image Processing / Computer Vision

    • Deformable 3-D mesh models​.

    • 3-D keypoint detectors​.

    • Object detection and tracking in 2- and 3-D images.

    • Spatiotemporal image analysis.

    • 3-D image segmentation.

    • Directional 3-D filtering (Gabor and log-Gabor filter banks)

  • Machine Learning / Deep Learning

    • Support Vector Machines (SVMs).

    • Variational Deep Autoencoders​.

    • Convolutional Neural Networks (CNN), Generative Adversarial Networks (GANs).

    • Smart data augmentation.

  • Statistical Pattern Recognition

    • Bayes and Naïve Bayes classifiers​.

    • Gaussian Mixture Models (GMM) and variants.

    • Principal Component Analysis.

  • Neurodegenerative diseases

    • Structural MR brain image analysis using Deep Variational Autoencoders for brain aging prediction. 

    • Development of CNNs models for MR image classification in Alzheimer´s diseases.

    • Characterization of adult normal MR brain image.

    • Quantification of hippocampal volume in MR images using deformable mesh models.

    • Segmentation and classification (active and non-active) of Multiple Sclerosis lesions in MR images.

    • Mapping structural brain changes via 3-D keypoint analysis.

  • Intravital Microscopy (in vivo studies)

    • Detection and tracking of blood cell in in-vivo studies.

  • Other projects

    • Quantification of abdominal and thighs adipose tissues in CT images.

Research Areas
Research Topics
BIP News
Recent published work

1) Katia M. Poloni, Ricardo J. Ferrari, "A deep ensemble hippocampal CNN model for brain age estimation applied to Alzheimer's diagnosis", Expert Systems With Applications, 195 (116622), p.1-12, 2022.

2) Katia M. Poloni, Ricardo J. Ferrari, "Automated detection, selection and classification of hippocampal landmark points for the diagnosis of Alzheimer's disease", Computer Methods and Programs in Biomedicine, 214(106581), p. 1-14, 2022.

3) Katia M. Poloni, Italo A.D. de Oliveira, Roger Tam, Ricardo J. Ferrari, "Brain MR image classification for Alzheimer's disease diagnosis using structural hippocampal asymmetrical attributes from directional 3-D log-Gabor filter responses", Neurocomputing, 419, p.126-135, 2021.


Interesting Reading
Deep Learning
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions.


Financial Support:
  • YouTube Limpa

Washington Luís Rd., km 235 / São Carlos, SP - Brazil