Arytenoid cartilage feature point detection using laryngeal 3D CT images in Parkinson's disease

Abstract

Parkinson's disease is a neurodegenerative disorder that results in progressive degeneration of nerve cells. It is generally associated with the deficiency of dopamine, a neurotransmitter involved in motor control of humans and thus affects the motor system. This results in abnormal vocal fold movements in majority of the Parkinson's patients. Analysis of vocal fold abnormalities may provide useful information to assess the progress of Parkinson's disease. This is accomplished by measuring the distance between the arytenoid cartilages during phonation. In order to automate this process of identifying arytenoid cartilages from CT images, in this work, a rule-based approach is proposed to detect the arytenoid cartilage feature points on either side of the airway. The proposed technique detects feature points by localizing the anterior commissure and analyzing airway boundary pixels to select the optimal feature point based on detected pixels. The proposed approach achieved 83.33% accuracy in estimating clinically-relevant feature points, making the approach suitable for automated feature point detection. To the best of our knowledge, this is the first such approach to detect arytenoid cartilage feature points using laryngeal 3D CT images.

Automated measurement of inter-arytenoid distance on 4D laryngeal CT: A validation study

“…We previously described an automated method which used classical imaging processing techniques to detect the most anteromedial aspect of the arytenoid cartilages. Comparing the feature points identified by this automated rule-based approach to fiducial markers manually placed by investigators, an accuracy of 83.33% was achieved within an error tolerance of 15 pixels (equivalent to an anatomical length of 3.33 mm) [ 21 ]. This represents the error in the placement of the individual fiducial markers; the IAD between the fiducial markers was not calculated in this study.…”

Multilevel thresholding and Morphological Relationship Approach for Automatic Detection of Anterior and Posterior Commissure in Mid-sagittal Brain MRI

“…AC and PC are a collection of transverse fibres connecting the two hemispheres of the brain and helps the exchange of information between the two [2]. Some roles of AC and PC include mapping of the human brain [3], medical image processing [4], and assisting in stereotactic neurosurgery [5,6].…”