Generalized framework for a user-aware interactive texture segmentation system

Arunkumar Gururajan; Hamed Sari-Sarraf; Eric F. Hequet

doi:10.1117/1.JEI.21.3.033017

16 August 2012 Generalized framework for a user-aware interactive texture segmentation system

Arunkumar Gururajan, Hamed Sari-Sarraf, Eric F. Hequet

Author Affiliations +

Journal of Electronic Imaging, Vol. 21, Issue 3, 033017 (August 2012). https://doi.org/10.1117/1.JEI.21.3.033017

Abstract

We present a new framework for an interactive image delineation technique, which we term as interactive texture-snapping system (IT-SNAPS). One of the unique features of IT-SNAPS stems from the fact that it can effectively aid the user in accurately segmenting images with complex texture, without placing undue burden on the user. This is made possible through the formulation of IT-SNAPS, which enables it to be user-aware, i.e., it unobtrusively elicits information from the user during the segmentation process, and hence, adapts itself on-the-fly to the boundary being segmented. In addition to generating an accurate segmentation, it is shown that the framework of IT-SNAPS allows for extraction of useful information post-segmentation, which can potentially assist in the development of customized automatic segmentation algorithms. The afore mentioned features of IT-SNAPS are demonstrated on a set of texture images, as well as on a real-world biomedical application. Using appropriate segmentation protocols in conjunction with expert-provided ground truth, experiments are designed to quantitatively evaluate and compare the segmentation accuracy and user-friendliness of IT-SNAPS with another popular interactive segmentation technique. Promising results indicate the efficacy of IT-SNAPS and its potential to positively impact a broad spectrum of computer vision applications.

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Arunkumar Gururajan, Hamed Sari-Sarraf, and Eric F. Hequet "Generalized framework for a user-aware interactive texture segmentation system," Journal of Electronic Imaging 21(3), 033017 (16 August 2012). https://doi.org/10.1117/1.JEI.21.3.033017

Published: 16 August 2012

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KEYWORDS

Image segmentation

Cervix

Image processing

Feature extraction

Algorithm development

Computer vision technology

Machine vision

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