@InProceedings{SautterRosaAlavSilv:2023:GrPaAn,
               author = "Sautter, Rubens Andreas and Rosa, Reinaldo Roberto and Alavarce, 
                         Debora Cristina and Silva, Daniel Guimar{\~a}es",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Hipocampus EdTech - 
                         Digital Learning} and {Hipocampus EdTech - Digital Learning}",
                title = "Gradient pattern analysis applied for computer vision in medical 
                         ultrasound diagnosis",
            booktitle = "Proceedings...",
                 year = "2023",
         organization = "Multi Conference on Computer Science and Information Systems, 
                         17.",
             keywords = "Gradient Pattern Analysis, Computer Vision, Supervised Machine 
                         Learning, 2D Endoscopic Ultrasound Biometry.",
             abstract = "This paper describes a new application of the technique known as 
                         Gradient Pattern Analysis (GPA), focused here on computer vision. 
                         In the GPA domain, the image is translated into a tessellation 
                         triangulation field based on the vectors positions that make up 
                         the gradient lattice of the matrix image. The GPA version 
                         considered here generates three attributes (G1, G2 and G3) that 
                         can be used as labels for a supervised machine-learning model. The 
                         case study presented here shows that GPA is a useful tool for 
                         real-time fetal biometry from 2D ultrasound images. The 
                         application in obstetrics indicates that the technique can also be 
                         useful for learning diagnostic imaging in gynecology, hepatology 
                         and oncology. The generalization of the technique to other 
                         applications in practical learning in health is discussed.",
  conference-location = "Porto, Portugal",
      conference-year = "15-18 July 2023",
           targetfile = "CGVCVIP2023_S_068.pdf",
        urlaccessdate = "15 jun. 2024"
}