Obtaining the glottal space segmentation is essential to characterize morphological disorders of vocal folds. In this study, the tested images are been acquired by direct optical inspection of the glottis using an endoscope and most of them are very poor quality. The application of motion estimation is very useful to segment the vocal folds endoscopic videos without user interaction. This approach involves three process steps: 1) Wiener motion estimator--to shift the measurement the next frame regarding to the current frame, and look for similarities between them. The best matching will accurate a shift equal to the displacement vector of the object; 2) Segmentation using motion estimation results and applying Gabor filtering; 3) Experimental results to demonstrate that the proposed method is effective. Our proposal works correctly with 95% of database test videos and it shows a great advance in design, and in the nearby future, a complete method to diagnose vocal folds pathologies.
The present study describes a robust hierarchical motion estimation algorithm in noisy image sequences using the bispectrum. The motion can be characterized by an affine model and the parameters of an affine motion model are estimated by means third-order auto-bispectrum and cross-bispectrum measures. The basic components of this framework to obtain motion vectors are (i) pyramid construction, (ii) motion estimation and (iii) coarse-to-fine refinement. The entire motion is decomposed as a global and a local motion field, which helps accurately obtain high resolution estimates for the local motion field. Simulation results are presented and compared to those obtained from the phase correlation algorithm. The results demonstrate that the proposed method is more suited than the phase correlation algorithm to analyses complex noisy image sequences. On the other hand, our method produces smoother displacement vector field with a more accurate measure of object motion in different signal-to-noise ratio scenarios.
This paper presents the study of vocal videostroboscopic videos to detect morphological pathologies using a combination of motion information and segmentation. The motion permits us to obtain the keyframes of total (or minimum) closure and maximum opening and to have the initialization for the segmentation process. The segmentation is made analyzing the image textures applying Gabor filtering. After this stage, where the pathology is (if it exists) is delimited using derivative techniques and interpolation. Finally, the pathology is localized and we can extract some features of each fold.
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