title: Computer-Aided Segmentation of the Mid-Brain in Trans-Cranial Ultrasound Images.
abstract: This paper presents a novel and rapid method developed for semi-automated segmentation of the mid-brain region in B-mode trans-cranial ultrasound (TCS) images. TCS is a relatively new neuroimaging tool having promising application in early diagnosis of Parkinson's disease. The quality of TCS images is much lower compared with the ultrasound images obtained during scanning of the soft tissues; the structures of interest in TCS are difficult to extract and to evaluate. The combination of an experience-based statistical shape model and intensity-amplitude invariant edge detector was proposed for the extraction of fuzzy boundaries of the mid-brain in TCS images. A statistical shape model was constructed using 90 manual delineations of the mid-brain region made by professional neurosonographer. Local phase-based edge detection strategy was applied for determination of plausible mid-brain boundary points used for statistical shape fitting. The proposed method was tested on other 40 clinical TCS images evaluated by two experts. The obtained averaged results of segmentation revealed that the differences between manual and automated measurements are statistically insignificant (p > 0.05).
authors.affiliation: Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania. Electronic address: andrius.sakalauskas@ktu.lt.
authors.forename: Andrius
authors.initials: A
authors.lastname: Sakalauskas
authors.affiliation: Department of Neurology, Lithuanian University of Health Sciences, Academy of Medicine, Kaunas, Lithuania.
authors.forename: Kristina
authors.initials: K
authors.lastname: Laučkaitė
authors.affiliation: Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania.
authors.forename: Arūnas
authors.initials: A
authors.lastname: Lukoševičius
authors.affiliation: Department of Neurology, Lithuanian University of Health Sciences, Academy of Medicine, Kaunas, Lithuania.
authors.forename: Daiva
authors.initials: D
authors.lastname: Rastenytė
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D004453:Echoencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: B-mode ultrasound; Edge detection; Local phase; Midbrain; Statistical shapes