Tractome: a visual data mining tool for brain connectivity analysis

• Published in: Data mining and knowledge discovery 2015-09, Vol.29 (5), p.1258-1279
• Main Authors: Porro-Muñoz, Diana, Olivetti, Emanuele, Sharmin, Nusrat, Nguyen, Thien Bao, Garyfallidis, Eleftherios, Avesani, Paolo
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Automation; Brain; Chemistry and Earth Sciences; Computer Science; Data mining; Data Mining and Knowledge Discovery; Datasets; Exploration; Information Storage and Retrieval; Magnetic resonance imaging; Physics; Statistics for Engineering; Visual; Visualization
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-015-0408-z

Diffusion magnetic resonance imaging data allows reconstructing the neural pathways of the white matter of the brain as a set of 3D polylines. This kind of data sets provides a means of study of the anatomical structures within the white matter, in order to detect neurologic diseases and understand the anatomical connectivity of the brain. To the best of our knowledge, there is still not an effective or satisfactory method for automatic processing of these data. Therefore, a manually guided visual exploration of experts is crucial for the purpose. However, because of the large size of these data sets, visual exploration and analysis has also become intractable. In order to make use of the advantages of both manual and automatic analysis, we have developed a new visual data mining tool for the analysis of human brain anatomical connectivity. With such tool, humans and automatic algorithms capabilities are integrated in an interactive data exploration and analysis process. A very important aspect to take into account when designing this tool, was to provide the user with comfortable interaction. For this purpose, we tackle the scalability issue in the different stages of the system, including the automatic algorithm and the visualization and interaction techniques that are used.