Conference proceeding
Multiobject relative fuzzy connectedness and its implications in image segmentation
Proceedings of SPIE, Vol.4322(1), pp.204-213
Medical Imaging 2001: Image Processing
07/03/2001
DOI: 10.1117/12.431090
Abstract
The notion of fuzzy connectedness captures the idea of hanging-togetherness of image elements in an object by assigning a strength of connectedness to every possible path between every possible pair of image elements. This concept leads to powerful image segmentation algorithms based on dynamic programming whose effectiveness has been demonstrated on 1000s of images in a variety of applications. In a previous framework, we introduced the notion of relative fuzzy connectedness for separating a foreground object from a background object. In this framework, an image element c is considered to belong to that among these two objects with respect to whose reference image element c has the higher strength of connectedness. In fuzzy connectedness, a local fuzzy reflation called affinity is used on the image domain. This relation was required for theoretical reasons to be of fixed form in the previous framework. In the present paper, we generalize relative connectedness to multiple objects, allowing all objects (of importance) to compete among themselves to grab membership of image elements based on their relative strength of connectedness to reference elements. We also allow affinity to be tailored to the individual objects. We present a theoretical and algorithmic framework and demonstrate that the objects defined are independent of the reference elements chosen as long as they are not in the fuzzy boundary between objects. Examples from medical imaging are presented to illustrate visually the effectiveness of multiple object relative fuzzy connectedness. A quantitative evaluation based on 160 mathematical phantom images demonstrates objectively the effectiveness of relative fuzzy connectedness with object- tailored affinity relation.
Details
- Title: Subtitle
- Multiobject relative fuzzy connectedness and its implications in image segmentation
- Creators
- Jayaram K Udupa - Univ. of Pennsylvania (USA)Punam K Saha - Univ. of Pennsylvania (USA)
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of SPIE, Vol.4322(1), pp.204-213
- Conference
- Medical Imaging 2001: Image Processing
- DOI
- 10.1117/12.431090
- ISSN
- 0277-786X
- Language
- English
- Date published
- 07/03/2001
- Academic Unit
- Electrical and Computer Engineering; Radiology
- Record Identifier
- 9984051993402771
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