A SUPERVOXEL-BASED APPROACH FOR UNSUPERVISED ABNORMAL ASYMMETRY DETECTION IN MR IMAGES OF THE BRAIN

Author(s)

Martins, Samuel B. ; Ruppert, Guilherme ; Reis, Fabiano ; Yasuda, Clarissa L. ; Falcao, Alexandre X. ; IEEE

• Document type: Journal article
• Source: 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019); v. N/A, p. 4-pg., 2019-01-01.

Abstract

Several pathologies are associated with abnormal asymmetries in brain images and their automated detection can improve diagnosis, segmentation, and automatic analysis of abnormal brain tissues (e.g., lesions). In this paper, we introduce a fully unsupervised supervoxel-based approach for abnormal asymmetry detection in MR images of the brain. Also, we present a new method for symmetrical supervoxel extraction called SymmISF. The experiments over a large set of MR-T1 images reveal a higher detection rates and considerably less false positives in comparison to a deep learning auto-encoder approach. (AU)

• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
• Grantee: Fernando Cendes
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC