@Article{EAJAM-6-1,
author = {},
title = {Integrating Feature Direction Information with a Level Set Formulation for Image Segmentation},
journal = {East Asian Journal on Applied Mathematics},
year = {2016},
volume = {6},
number = {1},
pages = {1--22},
abstract = {<p style="text-align: justify;">A feature-dependent variational level set formulation is proposed for image
segmentation. Two second order directional derivatives act as the external constraint in
the level set evolution, with the directional derivative across the image features direction
playing a key role in contour extraction and another only slightly contributes. To
overcome the local gradient limit, we integrate the information from the maximal (in
magnitude) second-order directional derivative into a common variational framework.
It naturally encourages the level set function to deform (up or down) in opposite directions
on either side of the image edges, and thus automatically generates object contours.
An additional benefit of this proposed model is that it does not require manual initial
contours, and our method can capture weak objects in noisy or intensity-inhomogeneous
images. Experiments on infrared and medical images demonstrate its advantages.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.231114.240915a},
url = {https://global-sci.com/article/82738/integrating-feature-direction-information-with-a-level-set-formulation-for-image-segmentation}
}