title: Blob-like feature extraction and matching for brain MR images.
abstract: The cerebral cortex of the human brain is highly folded. It is useful for neuroscientists and clinical researchers to identify and/or quantify cortical folding patterns across individuals. The top (gyri) and bottom (sulci) of these folds resemble the "blob-like" features used in computer vision. In this article, we evaluate different blob detectors and descriptors on brain MR images, and introduce our own, the "brain blob detector and descriptor (BBDD)." For the first time blob detectors are considered as spatial filters under the scale-space framework and their impulse responses are manipulated for detecting the structures in our interest. The BBDD detector is tailored to the scale and structure of blob-like features that coincide with cortical folds, and its descriptors performed well at discriminating these features in our evaluation.
authors.affiliation: Cognitive Neuroscience Division, the Taub Institute, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA. qr2108@columbia.edu
authors.forename: Qolamreza R
authors.initials: QR
authors.lastname: Razlighi
authors.forename: Yaakov
authors.initials: Y
authors.lastname: Stern
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016011:Normal Distribution