Chapter title |
Disjunctive Normal Shape and Appearance Priors with Applications to Image Segmentation
|
---|---|
Chapter number | 84 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2015
|
DOI | 10.1007/978-3-319-24574-4_84 |
Pubmed ID | |
Book ISBNs |
978-3-31-924573-7, 978-3-31-924574-4
|
Authors |
Fitsum Mesadi, Mujdat Cetin, Tolga Tasdizen |
Editors |
Nassir Navab, Joachim Hornegger, William M. Wells, Alejandro F. Frangi |
Abstract |
The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. Active shape and appearance models require landmark points and assume unimodal shape and appearance distributions. Level set based shape priors are limited to global shape similarity. In this paper, we present a novel shape and appearance priors for image segmentation based on an implicit parametric shape representation called disjunctive normal shape model (DNSM). DNSM is formed by disjunction of conjunctions of half-spaces defined by discriminants. We learn shape and appearance statistics at varying spatial scales using nonparametric density estimation. Our method can generate a rich set of shape variations by locally combining training shapes. Additionally, by studying the intensity and texture statistics around each discriminant of our shape model, we construct a local appearance probability map. Experiments carried out on both medical and natural image datasets show the potential of the proposed method. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 7 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Doctoral Student | 1 | 14% |
Professor | 1 | 14% |
Student > Ph. D. Student | 1 | 14% |
Student > Master | 1 | 14% |
Researcher | 1 | 14% |
Other | 1 | 14% |
Unknown | 1 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 2 | 29% |
Engineering | 2 | 29% |
Medicine and Dentistry | 1 | 14% |
Agricultural and Biological Sciences | 1 | 14% |
Unknown | 1 | 14% |