2016 IEEE International Conference on Image Processing (ICIP) 2016
DOI: 10.1109/icip.2016.7533136
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An automatic 3D CT/PET segmentation framework for bone marrow proliferation assessment

Abstract: Clinical assessment of bone marrow is limited by an inability to evaluate the marrow space comprehensively and dynamically and there is no current method for automatically assessing hematopoietic activity within the medullary space. Evaluating the hematopoietic space in its entirety could be applicable in blood disorders, malignancies, infections, and medication toxicity. In this paper, we introduce a CT/PET 3D automatic framework for measurement of the hematopoietic compartment proliferation within osseous si… Show more

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Cited by 6 publications
(2 citation statements)
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“…Hybrid segmenting Kidney abnormalities Matlab based on trial and error method. Another work uses a 3D bilateral filter to smooth spurious artifacts while preserving the strong edges of the cortical bone tissue (Nguyen et al, 2016). Sahadevan et al (2016) use Bilateral filter as first step smoothing and maintaining edges and the support vector machine (SVM) classifier as second step to test benchmark hyperspectral image taken from the airborne spectrometer.…”
Section: Introductionmentioning
confidence: 99%
“…Hybrid segmenting Kidney abnormalities Matlab based on trial and error method. Another work uses a 3D bilateral filter to smooth spurious artifacts while preserving the strong edges of the cortical bone tissue (Nguyen et al, 2016). Sahadevan et al (2016) use Bilateral filter as first step smoothing and maintaining edges and the support vector machine (SVM) classifier as second step to test benchmark hyperspectral image taken from the airborne spectrometer.…”
Section: Introductionmentioning
confidence: 99%
“…One of the denoising filters is bilateral filter which reduces noise with remaining sharp edges of the objects. Besides, Nguyen et al [4] denoised specific artifacts and segmented the full body bone structure by employing 3D bilateral filter and 3D graph-cut, respectively. On the other hand, Sahadevan et al [5] increased the accuracy of super vector machine classifier using a bilateral filter which merges spatial contextual information to spectral domain.…”
Section: Introductionmentioning
confidence: 99%