Analysis of error in measuring laryngeal size parameters from compressed imagery

The accurate, consistent measurement of laryngeal size parameters is key to the quantification of phonatory disorders from laryngeal endoscopic imagery. Unfortunately, laryngoscopic images contain a wide variety of distortions introduced by endoscope optics (e.g., barrel distortion), systematic effects such as mucus strands adhering to the endoscope lens, electronic noise in the camera or digitization hardware, and color distortions resulting from optical, camera, or digitization errors. These difficulties are compounded by representational errors introduced during image archival or telemedicine-based manipulation of endoscopic imagery, e.g., when images are compressed, stored, then decompressed using lossy transformations. A variety of researchers, in particular Omori et al., have studied the measurement of laryngeal parameters from a variety of image sources. Unfortunately, such analyses do not account for the effects of image compression/decompression. In this paper, previous research is extended to include estimation of errors in the measurement of parameters such as glottal gap area and maximum vocal fold length from compressed laryngoscopic imagery. Compression transforms studied include JPEG and EBLAST, a relatively recent development in high-compression image transformation for communication along low-bandwidth channels. Error analysis emphasizes preservation of spatial and greylevel information in the decompressed imagery, as well as error in parameter measurement at various compression ratios. Manual as well as automatic methods of laryngeal parameter extraction are analyzed, including techniques based on spectral restriction applied to moderate-resolution RGB imagery (320×200 pixels). The analysis presented herein represents work-in-progress, and is not intended to represent a final implementation suitable for medical diagnostic or life-critical applications, but is advanced as a phenomenological overview of measurement error in the presence of image compression in a medical imaging application.