Abstract
PurposeThe study is conducted to identify the best corpus callosum (CC) sub-region that corresponds to highest callosal tissue alteration occurred due to Parkinsonism. In this regard the efficacy of local binary pattern (LBP) based texture analysis (TA) of CC is performed to quantify the changes in topographical distribution of callosal fiber connected to different regions of cortex. The extent of highest texture alteration in CC is used for differential diagnosis.Materials and MethodsStudy included subjects with Parkinson’s disease (PD) (n = 20), and atypical Parkinsonian disorders – multiple system atrophy (MSA) (n = 20), Progressive supranuclear palsy (PSP) (n = 20), and healthy controls (n = 20). For each subject, we have automated the ROI extraction within mid-sagittal CC, followed by LBP TA. Two-class support vector machine (SVM) classification for each disorder as against HC is performed using extracted LBP features like energy and entropy. Correct classification ratio (CCR) is computed as the fraction of correctly classified ROIs at each of the CC sub-regions based on well-known Witelson and Hofer schemes. Based on CCR values, the “Scatter Index (SI)” is proposed to capture how localized (closer to 0) or scattered (closer to 1) the textural changes are among the CC sub-regions, across all subjects per class. The CCR values are further utilized to classify the disease groups.ResultsHighest alteration of texture is observed in mid-body of CC. The consistency of this finding is quantified using SI for all subjects in a specific class that results more localized textural changes in PSP (15%) and MSA (25%), in comparison to PD (47%). Classification among disease groups results maximum classification accuracy of 90% in classifying PSP from PD-NC.ConclusionOur result demonstrates the efficacy of proposed methodology in analyzing tissue alteration in MRI of Parkinsonian disorders and thus has potential to become valuable tool in computer aided differential diagnosis.
Highlights
Parkinsonian disorders are chronic progressive neurodegenerative movement disorders, which are classically categorized into Parkinson’s disease (PD) and atypical Parkinsonian disorders such as Progressive supranuclear palsy (PSP), multiple system atrophy (MSA), etc. (Williams and Litvan, 2013)
Sections “corpus callosum (CC) Sub-Region Ranking Based on Classification Performance” and “Scatter Index: Quantification of Dispersion in Array of Importance (AI)” of this paper describe how the result of this classification is utilized to form a new statistical framework that could be useful for differential diagnosis
Using Energy the highest classification ratio” (CCR) values are obtained in sub-region R3 and R4 of Hofer’s CC sub-division scheme and in sub-region R2 to R4 of Witelson’s CC sub-division scheme
Summary
Parkinsonian disorders are chronic progressive neurodegenerative movement disorders, which are classically categorized into Parkinson’s disease (PD) and atypical Parkinsonian disorders such as Progressive supranuclear palsy (PSP), multiple system atrophy (MSA), etc. (Williams and Litvan, 2013). PD is typically characterized by levodopa responsive rigidity, bradykinesia, and tremor, whereas atypical Parkinsonian disorders present with several other motor systems and tend to be poorly responsive to levodopa. These disorders have been extensively studied using a multitude of structural neuroimaging sequences and modalities of analysis (Paviour et al, 2006; Heim et al, 2018; Rispoli et al, 2018); texture analysis (TA), which is a quantitative method of characterizing tissue types based on texture, has seldom been performed for Parkinsonian disorders. In diseases such as non-Hodgkin lymphoma, mild traumatic brain injury, and multiple sclerosis, TA has been able to identify lesions which are not identifiable by the naked eye (Harrison, 2011)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
