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      Quantitative investigation of the vibration pattern of the substitute voice generator.

      IEEE transactions on bio-medical engineering
      Aged, Algorithms, Computer Simulation, Esophagus, pathology, physiopathology, surgery, Humans, Image Interpretation, Computer-Assisted, methods, Laryngoscopy, Larynx, Larynx, Artificial, Male, Models, Biological, Speech Disorders, Speech Production Measurement, Speech, Esophageal, Vibration, Video Recording

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          Abstract

          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.

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              T-snakes: Topology adaptive snakes

              We present a new class of deformable contours (snakes) and apply them to the segmentation of medical images. Our snakes are defined in terms of an affine cell image decomposition (ACID). The 'snakes in ACID' framework significantly extends conventional snakes, enabling topological flexibility among other features. The resulting topology adaptive snakes, or 'T-snakes', can be used to segment some of the most complex-shaped biological structures from medical images in an efficient and highly automated manner.
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