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Description: NetBeans Project that clusters documents according to similarity calculated by dice coefficient.
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Size: 20480 |
Author: Gaurav |
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Description: Abstract—In this study, we propose a novel approach for accurate
3-D organ segmentation in the CT scan volumes. Instead of
using the organ’s prior information directly in the segmentation
process, here we utilize the knowledge of the organ to validate a
large number of potential segmentation outcomes that are generated
by a generic segmentation process. For this, an organ space
is generated based on the principal component analysis approach
using which the fidelity of each segment to the organ is measured.
We detail applications of the proposed method for the 3-D segmentation
of human kidney and liver in computed tomography scan
volumes. For evaluation, the public database of theMICCAI’s 2007
grand challenge workshop has been incorporated. Implementation
results show an average Dice similarity measure of 0.90 for the segmentation
of the kidney. For the liver segmentation, the proposed
algorithm achieves an average volume overlap error of 8.7 and
an average surface distance of 1.51 mm.-Abstract—In this study, we propose a novel approach for accurate
3-D organ segmentation in the CT scan volumes. Instead of
using the organ’s prior information directly in the segmentation
process, here we utilize the knowledge of the organ to validate a
large number of potential segmentation outcomes that are generated
by a generic segmentation process. For this, an organ space
is generated based on the principal component analysis approach
using which the fidelity of each segment to the organ is measured.
We detail applications of the proposed method for the 3-D segmentation
of human kidney and liver in computed tomography scan
volumes. For evaluation, the public database of theMICCAI’s 2007
grand challenge workshop has been incorporated. Implementation
results show an average Dice similarity measure of 0.90 for the segmentation
of the kidney. For the liver segmentation, the proposed
algorithm achieves an average volume overlap error of 8.7 and
an average surface distance of 1.51 mm.
Platform: |
Size: 865280 |
Author: robin |
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Description: Magnetic resonance (MR) images can be used to detect lesions in the brains of multiple sclerosis (MS)
patients and is essential for diagnosing the disease and monitoring its progression. An automatic method is
presented for segmentation of MS lesions in multispectral MR images. Firstly a PD-w image is subtracted its
corresponding T1-w image to get an image in which the cerebral spinal fluid (CSF) is enhanced. Then based on
kernel fuzzy c-means (KFCM) algorithm, the enhanced image and the corresponding T2-w image are segmented
respectively to extract the CSF region and the CSF combining MS lesions region. A raw MS lesions image is
obtained by subtracting the CSF region CSF combining MS region. By applying median filter and
thresholding to the raw image, the MS lesions are detected finally. Results are quantitatively uated on
BrainWeb images using Dice similarity coefficient (DSC). Finally, the potential of the method as well as its
limitations are discussed.-Magnetic resonance (MR) images can be used to detect lesions in the brains of multiple sclerosis (MS)
patients and is essential for diagnosing the disease and monitoring its progression. An automatic method is
presented for segmentation of MS lesions in multispectral MR images. Firstly a PD-w image is subtracted its
corresponding T1-w image to get an image in which the cerebral spinal fluid (CSF) is enhanced. Then based on
kernel fuzzy c-means (KFCM) algorithm, the enhanced image and the corresponding T2-w image are segmented
respectively to extract the CSF region and the CSF combining MS lesions region. A raw MS lesions image is
obtained by subtracting the CSF region CSF combining MS region. By applying median filter and
thresholding to the raw image, the MS lesions are detected finally. Results are quantitatively uated on
BrainWeb images using Dice similarity coefficient (DSC). Finally, the potential of the method as well as its
limitations are discussed.
Platform: |
Size: 2048 |
Author: mahsy |
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