Dataset Information

Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means.

ABSTRACT:

Objective

We introduce descriptor-based segmentation that extends existing patch-based methods by combining intensities, features, and location information. Since it is unclear which image features are best suited for patch selection, we perform a broad empirical study on a multitude of different features.

Methods

We extend nonlocal means segmentation by including image features and location information. We search larger windows with an efficient nearest neighbor search based on kd-trees. We compare a large number of image features.

Results

The best results were obtained for entropy image features, which have not yet been used for patch-based segmentation. We further show that searching larger image regions with an approximate nearest neighbor search and location information yields a significant improvement over the bounded nearest neighbor search traditionally employed in patch-based segmentation methods.

Conclusion

Features and location information significantly increase the segmentation accuracy. The best features highlight boundaries in the image.

Significance

Our detailed analysis of several aspects of nonlocal means-based segmentation yields new insights about patch and neighborhood sizes together with the inclusion of location information. The presented approach advances the state-of-the-art in the segmentation of parotid glands for radiation therapy planning.

SUBMITTER: Wachinger C

PROVIDER: S-EPMC5469701 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means.

Wachinger Christian C Brennan Matthew M Sharp Greg C GC Golland Polina P

IEEE transactions on bio-medical engineering 20160916 7

<h4>Objective</h4>We introduce descriptor-based segmentation that extends existing patch-based methods by combining intensities, features, and location information. Since it is unclear which image features are best suited for patch selection, we perform a broad empirical study on a multitude of different features.<h4>Methods</h4>We extend nonlocal means segmentation by including image features and location information. We search larger windows with an efficient nearest neighbor search based on k ...[more]

PMID: 28113224

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Project description:Magnetic resonance (MR) images are often corrupted by Rician noise which degrades the accuracy of image-based diagnosis tasks. The nonlocal means (NLM) method is a representative filter in denoising MR images due to its competitive denoising performance. However, the existing NLM methods usually exploit the gray-level information or hand-crafted features to evaluate the similarity between image patches, which is disadvantageous for preserving the image details while smoothing out noise. In this paper, an improved nonlocal means method is proposed for removing Rician noise in MR images by using the refined similarity measures. The proposed method firstly extracts the intrinsic features from the pre-denoised image using a shallow convolutional neural network named Laplacian eigenmaps network (LEPNet). Then, the extracted features are used for computing the similarity in the NLM method to produce the denoised image. Finally, the method noise of the denoised image is utilized to further improve the denoising performance. Specifically, the LEPNet model is composed of two cascaded convolutional layers and a nonlinear output layer, in which the Laplacian eigenmaps are employed to learn the filter bank in the convolutional layers and the Leaky Rectified Linear Unit activation function is used in the final output layer to output the nonlinear features. Due to the advantage of LEPNet in recovering the geometric structure of the manifold in the low-dimension space, the features extracted by this network can facilitate characterizing the self-similarity better than the existing NLM methods. Experiments have been performed on the BrainWeb phantom and the real images. Experimental results demonstrate that among several compared denoising methods, the proposed method can provide more effective noise removal and better details preservation in terms of human vision and such objective indexes as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

Dataset Information

Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means.

Objective

Methods

Results

Conclusion

Significance

Publications

Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means.

Similar Datasets

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