A New Statistical Framework for Corpus Callosum Sub-Region Characterization Based on LBP Texture in Patients With Parkinsonian Disorders: A Pilot Study

Abstract

The 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 Methods: Study 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.Results: Highest 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. Conclusion:Our 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.

Segmental Alterations of the Corpus Callosum in Progressive Supranuclear Palsy: A Multiparametric Magnetic Resonance Imaging Study

“…The findings in PSP, however, were beyond these PD-associated alterations, since in PSP patients, the introduction of a combined parameter score led to a comprehensive textural/microstructural data analysis which showed alterations in frontal CC areas, most prominent in the callosal area II which were significant in comparison to PD patients. The more prominent CC alterations in PSP are in a line of agreement with the findings of a pilot study employing binary pattern-based texture analysis in both disorders which described more localized tissue alterations in mid-callosal regions in PSP (Bhattacharya et al, 2020). Our findings are also in a line of agreement with a fixelbased study in patients with neurodegenerative parkinsonism which demonstrated a reduction of a combined measure of fiber density and cross-section in the body of the CC in PSP (Nguyen et al, 2021).…”

A Method for Identifying Flaws on Product Surface Based on Spatial Connectivity Domain

“…The Tamura texture [16] analysis method is effective to analyze the contrast, coarseness and directionality of the image, but it is poor in recognition of the linelikeness, regularity and roughness of the image. The algorithm of extracting texture feature based on Gabor [17] wavelet transform has better retrieval effect in frequency domain, and it can eliminate redundant information. But the higher the dimension of feature vector increases, the more slowly the retrieval speed becomes.…”

“…3 Figure 4. In addition, the texture of flaws on the product surface is very similar to the background texture, so it is very difficult to extract feature of flaws by the extracting texture algorithm [17,18]. The defective product could not be identified, as shown in Sect.…”