<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Local Segmentation is the fundamental task for image processing. Consider to the problem of low segmentation precision and contour control instability for image local segmentation, a local segmentation theory is researched that based on SSCACM (spectral clustering with spatial coherence property jointing active contour model). First, by applying spatial coherence property constraint of image pixels to spectral clustering, an adaptive similarity function is constructed and the corresponding spectral clustering algorithm is used to extract initial contour of the local region of an image. Then, the NBACM (narrow band active contour model) is combined with the priori information of initial contour to evolve contour curve to get the segmentation result. At last, the local segmentation experiment is realized on synthetic images and medical images. The experimental results show that the method proposed can extract contour accurately and can improve the effectiveness and robust for image local segmentation.</p>
</abstract>

Local Segmentation is the fundamental task for image processing. Consider to the problem of low segmentation precision and contour control instability for image local segmentation, a local segmentation theory is researched that based on SSCACM (spectral clustering with spatial coherence property jointing active contour model). First, by applying spatial coherence property constraint of image pixels to spectral clustering, an adaptive similarity function is constructed and the corresponding spectral clustering algorithm is used to extract initial contour of the local region of an image. Then, the NBACM (narrow band active contour model) is combined with the priori information of initial contour to evolve contour curve to get the segmentation result. At last, the local segmentation experiment is realized on synthetic images and medical images. The experimental results show that the method proposed can extract contour accurately and can improve the effectiveness and robust for image local segmentation.

Spectral Clustering with Spatial Coherence Property Jointing to Active Contour Model for Image Local SE Gmentation

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Local Segmentation is the fundamental task for image processing. Consider to the problem of low segmentation precision and contour control instability for...

Spectral Clustering with Spatial Coherence Property Jointing to Active Contour Model for Image Local SE Gmentation

Publié en ligne: 01 déc. 2016

Pages: 1731 - 1749

Reçu: 06 janv. 2016

Accepté: 04 oct. 2016

DOI: https://doi.org/10.21307/ijssis-2017-937

Mots clés
Adaptive similarity function, Spatial coherence property constraints, Adaptive speed operator, Spectral clustering, Active Contour model

© 2016 Guang Hu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Spectral Clustering with Spatial Coherence Property Jointing to Active Contour Model for Image Local SE Gmentation

Publié en ligne: 01 déc. 2016

Pages: 1731 - 1749

Reçu: 06 janv. 2016

Accepté: 04 oct. 2016

DOI: https://doi.org/10.21307/ijssis-2017-937

Mots clésAdaptive similarity function, Spatial coherence property constraints, Adaptive speed operator, Spectral clustering, Active Contour model

© 2016 Guang Hu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Adaptive similarity function, Spatial coherence property constraints, Adaptive speed operator, Spectral clustering, Active Contour model