title: Automated segmentation of transcranial sonographic images in the diagnostics of Parkinson's disease.
abstract: Images captured during routine clinical transcranial sonography (TCS) examination are of a low resolution, so can be confusing for diagnostic evaluations. Manual segmentation of brain structures (areas of the midbrain and substantia nigra (SN)) that are of special interest cause inter-observer and intra-observer variability, thus restricting the reliability of Parkinson disease (PD) diagnostics. This paper presents a new technique for automated segmentation applicable to low resolution sonographic images, and particularly to brain structures related to PD. The segmentation was performed by a modified shape-based active contour (AC) segmentation algorithm. In order to suppress the speckle noise and to improve the AC segmentation, a pre-processing technique based on the averaging of adjusted spatially varying TCS images is proposed. The latter technique was tested on clinical TCS images. The results of the automated segmentation were compared with the manual markings. Two experts on the 40TCS images performed these markings. The comparison showed that an automated method is effective when segmentation of the midbrain is performed (averaged overlap between regions obtained automatically and outlined manually was 73.10±7.45%). The results of the segmentation of the SN area showed that a sufficiently correct contour of this area could also be obtained, but the accuracy of the segmentation is related to the image quality. It should be emphasised that the main difficulty in evaluating the accuracy of automated segmentation of the SN was the indefinite "gold standard" (variation between the measurements of two experts with different experience was found). And, therefore, the diagnostic reliability of the proposed technique was inconclusive.
authors.affiliation: Biomedical Engineering Institute, Kaunas University of Technology, Studentu Str. 65, Kaunas LT-51359, Lithuania. sakalauskas.andrius@yahoo.com
authors.forename: Andrius
authors.initials: A
authors.lastname: Sakalauskas
authors.forename: Arūnas
authors.initials: A
authors.lastname: Lukoševičius
authors.forename: Kristina
authors.initials: K
authors.lastname: Laučkaitė
authors.forename: Darius
authors.initials: D
authors.lastname: Jegelevičius
authors.forename: Saulius
authors.initials: S
authors.lastname: Rutkauskas
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008636:Mesencephalon / Q000000981:diagnostic imaging; D008875:Middle Aged; D010300:Parkinson Disease / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated; D013378:Substantia Nigra / Q000000981:diagnostic imaging; D017585:Ultrasonography, Doppler, Transcranial / Q000379:methods*