Bias field estimation and segmentation of MR image using modified fuzzy-C means algorithms

Aparajeeta, Jeetashree; Nanda, Pradipta Kumar; Das, Niva

doi:10.1109/icapr.2015.7050650

2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR) 2015

DOI: 10.1109/icapr.2015.7050650

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Bias field estimation and segmentation of MR image using modified fuzzy-C means algorithms

Jeetashree Aparajeeta

Pradipta Kumar Nanda

Niva Das

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2017

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“…The basic framework is inspired from the analogy between shading and biasfield effect -which is a low frequency noise signal -observed in MR images. Several variants of FCM have been proposed in the literature for estimating bias-field in MR images [18,19,20,21]. We extend the application of those frameworks on craniofacial CBCT images.…”

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A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images

Ashfaq

Adler

2017

IFMBE Proceedings

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Abstract. CBCT images suffer from acute shading artifacts primarily due to scatter. Numerous image-domain correction algorithms have been proposed in the literature that use patient-specific planning CT images to estimate shading contributions in CBCT images. However, in the context of radiosurgery applications such as gamma knife, planning images are often acquired through MRI which impedes the use of polynomial fitting approaches for shading correction. We present a new shading correction approach that is independent of planning CT images. Our algorithm is based on the assumption that true CBCT images follow a uniform volumetric intensity distribution per material, and scatter perturbs this uniform texture by contributing cupping and shading artifacts in the image domain. The framework is a combination of fuzzy C-means coupled with a neighborhood regularization term and Otsu's method. Experimental results on artificially simulated craniofacial CBCT images are provided to demonstrate the effectiveness of our algorithm. Spatial non-uniformity is reduced from 16% to 7% in soft tissue and from 44% to 8% in bone regions. With shading-correction, thresholding based segmentation accuracy for bone pixels is improved from 85% to 91% when compared to thresholding without shading-correction. The proposed algorithm is thus practical and qualifies as a plug and play extension into any CBCT reconstruction software for shading correction.

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A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images

Ashfaq

Adler

2017

IFMBE Proceedings

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Bias field estimation and segmentation of MR image using modified fuzzy-C means algorithms

Cited by 1 publication

References 13 publications

A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images

A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images

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