title: An improved lesion detection approach based on similarity measurement between fuzzy intensity segmentation and spatial probability maps.
abstract: The application of automatic segmentation methods in lesion detection is desirable. However, such methods are restricted by intensity similarities between lesioned and healthy brain tissue. Using multi-spectral magnetic resonance imaging (MRI) modalities may overcome this problem but it is not always practicable. In this article, a lesion detection approach requiring a single MRI modality is presented, which is an improved method based on a recent publication. This new method assumes that a low similarity should be found in the regions of lesions when the likeness between an intensity based fuzzy segmentation and a location based tissue probabilities is measured. The usage of a normalized similarity measurement enables the current method to fine-tune the threshold for lesion detection, thus maximizing the possibility of reaching high detection accuracy. Importantly, an extra cleaning step is included in the current approach which removes enlarged ventricles from detected lesions. The performance investigation using simulated lesions demonstrated that not only the majority of lesions were well detected but also normal tissues were identified effectively. Tests on images acquired in stroke patients further confirmed the strength of the method in lesion detection. When compared with the previous version, the current approach showed a higher sensitivity in detecting small lesions and had less false positives around the ventricle and the edge of the brain.
authors.affiliation: Department of Psychology, University of Surrey, Guildford GU2 7XH, UK. shan.shen@surrey.ac.uk
authors.forename: Shan
authors.initials: S
authors.lastname: Shen
authors.forename: Andre J
authors.initials: AJ
authors.lastname: Szameitat
authors.forename: Annette
authors.initials: A
authors.lastname: Sterr
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000175:diagnosis*; D003627:Data Interpretation, Statistical; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique