Dictionary Learning Level Set

We propose a novel region based segmentation technique using dictionary learning. In a previous work we have developed a method which uses a set of pre-specified Legendre basis functions to perform region based segmentation of an object in presence of heterogeneous illumination. We hypothesize that...

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Bibliographic Details
Published in:IEEE signal processing letters 2015-11, Vol.22 (11), p.2034-2038
Main Authors: Sarkar, Rituparna, Mukherjee, Suvadip, Acton, Scott T.
Format: Article
Language:English
Subjects:
Active contour
Dictionaries
dictionary learning
Image segmentation
Imaging
Level set
Lighting
segmentation
Signal processing algorithms
Ultrasonic imaging
ultrasound
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Summary:We propose a novel region based segmentation technique using dictionary learning. In a previous work we have developed a method which uses a set of pre-specified Legendre basis functions to perform region based segmentation of an object in presence of heterogeneous illumination. We hypothesize that in problems where a set of training images for the object is available for analysis (such as depth image sequence of blood vessels via ultrasound imaging), segmentation accuracy can be significantly improved by learning the basis functions instead of specifying them implicitly. The salient idea of this letter is to compute the optimal set of functions to model the region intensities. Our solution to this problem involves the integration of a level set segmentation methodology with the dictionary learning framework. This provides an elegant solution to deal with intensity inhomogeneities prevalent in many imaging applications such as ultrasound and fluorescence microscopy. The proposed algorithm, Dictionary Learning Level Set (DL2S) is used to segment ultrasound images of blood vessels captured using low cost, portable ultrasound devices employed in a phlebotomy application. Qualitative and quantitative results obtained from this dataset suggest efficacy of D2LS with an associated improvement in the average Dice index of 12% over the relevant competitors.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2015.2454991