title   
  

Nonparametric joint shape and feature priors for image segmentation

Erdil, Ertunç and Ghani, Muhammad Usman and Rada, Lavdie and Argunşah, Ali Özgür and Ünay, Devrim and Taşdizen, Tolga and Çetin, Müjdat (2017) Nonparametric joint shape and feature priors for image segmentation. IEEE Transactions on Image Processing, 26 (11). pp. 5312-5323. ISSN 1057-7149 (Print) 1941-0042 (Online)

[img]PDF - Registered users only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
3240Kb

Official URL: http://dx.doi.org/10.1109/TIP.2017.2728185

Abstract

In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes ( classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape-and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.

Item Type:Article
Uncontrolled Keywords:Level set; disjunctive normal forms; segmentation; parametric level set; multiphase level set; variational; Bayesian methods
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
ID Code:33800
Deposited By:Müjdat Çetin
Deposited On:10 Sep 2017 16:06
Last Modified:10 Sep 2017 16:06

Repository Staff Only: item control page