Pathologic changes within the organic constitution of vocal folds or a functional impairment of the larynx may result in disturbed or even irregular vocal fold vibrations. The consequences are perturbations of the acoustic speech signal which are perceived as a hoarse voice. By means of appropriate image processing techniques, the vocal fold dynamics are extracted from digital high-speed videos. This study addresses the approach to obtain a parametric description of the spatio-temporal characteristics of the vocal fold oscillations for the aim of classification. For this purpose a biomechanical vocal fold model is introduced. An automatic optimization procedure is developed for fitting the model dynamics to the observed vocal fold oscillations. Thus, the resulting parameter values represent a specific vibration pattern and serve as an objective quantification measure. Performance and reliability of the optimization procedure are validated with synthetically generated data sets. The high-speed videos of two normal voice subjects and six patients suffering from different voice disorders are processed. The resulting model parameters represent a rough approximation of physiological parameters along the entire vocal folds.

Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head-neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone-cartilage interfaces for potential cartilage segmentation.

Classification of vocal fold vibrations is an essential task of the objective assessment of voice disorders. For historical reasons, the conventional clinical examination of vocal fold vibrations is done during stationary, sustained phonation. However, the conclusions drawn from a stationary phonation are restricted to the observed steady-state vocal fold vibrations and cannot be generalized to voice mechanisms during running speech. This study addresses the approach of classifying real-time recordings of vocal fold oscillations during a nonstationary phonation paradigm in the form of a pitch raise. The classification is based on asymmetry measures derived from a time-dependent biomechanical two-mass model of the vocal folds which is adapted to observed vocal fold motion curves with an optimization procedure. After verification of the algorithm performance the method was applied to clinical problems. Recordings of ten subjects with normal voice and ten dysphonic subjects have been evaluated during stationary as well as nonstationary phonation. In the case of nonstationary phonation the model-based classification into "normal" and "dysphonic" succeeds in all cases, while it fails in the case of sustained phonation. The nonstationary vocal fold vibrations contain additional information about vocal fold irregularities, which are needed for an objective interpretation and classification of voice disorders.

Hoarseness in unilateral vocal fold paralysis is mainly due to irregular vocal fold vibrations caused by asymmetries within the larynx physiology. By means of a digital high-speed camera vocal fold oscillations can be observed in real-time. It is possible to extract the irregular vocal fold oscillations from the high-speed recordings using appropriate image processing techniques. An inversion procedure is developed which adjusts the parameters of a biomechanical model of the vocal folds to reproduce the irregular vocal fold oscillations. Within the inversion procedure a first parameter approximation is achieved through a knowledge-based algorithm. The final parameter optimization is performed using a genetic algorithm. The performance of the inversion procedure is evaluated using 430 synthetically generated data sets. The evaluation results comprise an error estimation of the inversion procedure and show the reliability of the algorithm. The inversion procedure is applied to 15 healthy voice subjects and 15 subjects suffering from unilateral vocal fold paralysis. The optimized parameter sets allow a classification of pathologic and healthy vocal fold oscillations. The classification may serve as a basis for therapy selection and quantification of therapy outcome in case of unilateral vocal fold paralysis.

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A model-based approach is proposed to objectively measure and classify vocal fold vibrations by left-right asymmetries along the anterior-posterior direction, especially in the case of nonstationary phonation. For this purpose, vocal fold dynamics are recorded in real time with a digital high-speed camera during phonation of sustained vowels as well as pitch raises. The dynamics of a multimass model with time-dependent parameters are matched to vocal fold vibrations extracted at dorsal, medial, and ventral positions by an automatic optimization procedure. The block-based optimization accounts for nonstationary vibrations and compares the vocal fold and model dynamics by wavelet coefficients. The optimization is verified with synthetically generated data sets and is applied to 40 clinical high-speed recordings comprising normal and pathological voice subjects. The resulting model parameters allow an intuitive visual assessment of vocal fold instabilities within an asymmetry diagram and are applicable to an objective quantification of asymmetries.

After a total excision of the larynx, mucosal tissue at the upper part of the esophagus can be used as a substitute voice generating element. The properties of the tissue dynamics are closely related to the substitute voice quality. The process of substitute voice is investigated by recording simultaneously the acoustic signal with a microphone and the vibrations of the voice generator with a digital high-speed camera. We propose an automatic image-processing technique which is applied to analyze the vibration pattern of the substitute voice generating element. First, an initialization step detects the voice generator within a high-speed sequence. Second, a combination of a threshold technique and an active contour algorithm tracks the tissue deformations of the substitute voice generator. The applicability of the algorithm is shown in three high-speed recordings. For the first time, tissue deformations of substitute voice generating elements are successfully tracked. The results of the image processing procedure are used to describe quantitatively the temporal properties of the substitute voice generator. Comparisons of the spectral components of tissue deformations and tracheoesophageal voice signals reveal the close relationship between the vibration pattern of the substitute voice generator and the quality of substitute voice.