Araştırma Makalesi
BibTex RIS Kaynak Göster

Medical image enhancement based on volumetric tissue segmentation fusion (Uni-stable 3D method)

Yıl 2023, Cilt: 4 Sayı: 2, 78 - 89, 21.12.2023

Ihab Elaff

https://doi.org/10.53525/jster.1250050

Öz

The 3D Uni-stable is a new method for 3D medical image enhancement which produces 3D Images of high contrast from the scanned anisotropic scaling images. This is done by estimating some intermediate slices through resizing the original scans. Rescaling has been achieved at three different levels: rescaling of eigenvalues of diffusion, rescaling the Scalar Indexes from the original eigenvalues, and rescaling the cluster maps of the segmentation of the original Scalar Indexes. Four interpolation methods have been employed at each level and four clustering algorithms have been employed in the process. The 3D Uni-stable image is almost universal as it combines variety of algorithms points of views into one 3D probability map. This reduces boundary-overlapping among different tissues, and hence improves the uniqueness of the segmentation problem solution. The stability factor of the 3D Uni-stable-Images is measured by maximum match analysis between the cluster maps which are generated from 3D Uni-stable images using variety of clustering methods with respect to true fact references for 5 different brains and the resultant standard deviations of Uni-stable images maximum match analysis in both threshold and tissue to brain ratio are much lower than Mean Diffusivity and Fractional Anisotropy scalar indexes for both CSF/non-CSF and WM/non-WM respectively

Anahtar Kelimeler

Uni-stable WM GM CSF Brain Segmentation

Kaynakça

  • [1] Doi K., Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics. 2017; 31(4):198-211. DOI: 10.1016/j.compmedimag.2007.02.002
  • [2] Li Q, Nishikawa RM, editors. Computer-Aided Detection and Diagnosis in Medical Imaging. Taylor & Francis, CRC Press, New York; 2015.
  • [3] Neubert A, Salvado O, Acosta O, Bourgeat P, Fripp J. Constrained reverse diffusion for thick slice interpolation of 3D volumetric MRI images. Computerized Medical Imaging and Graphics 2012; 36(2): 130-138. DOI: 10.1016/j.compmedimag.2011.08.004.
  • [4] Mori S. Introduction to Diffusion Tensor Imaging.1st ed., Oxford, UK: Elsevier, 2007.
  • [5] Liu T, Li H, Wong K, Tarokh A, Guo L, Wong S. Brain Tissue Segmentation Based on DTI Data. Neuroimage 2007; 38: 114-23. DOI: 10.1016/j.neuroimage.2007.07.002
  • [6] Zarei M, Johansen Berg H, Matthews PM. Diffusion Tensor Imaging and Tractography in Clinical Neurosciences. Iran J Radiol 2003; 1: 45-52.
  • [7] Khotanlou H, Colliot O, Atif J, Bloch I. 3D Brain Tumor Segmentation in MRI Using Fuzzy Classification, Symmetry Analysis and Spatially Constrained Deformable Models. Fuzzy Set Syst 2009; 160: 1457-1473. DOI:10.1016/j.fss.2008.11.016
  • [8] Dubey RB,Hanmandlu M,Gupta SK,Gupta SK. The Brain MR Image Segmentation Techniques and use of Diagnostic Packages. Acad Radiol. 2010; 17: 658 – 671. DOI:10.1016/j.acra.2009.12.017
  • [9] Getreuer P. Linear Methods for Image Interpolation, Image Processing On Line, 2011; 1:238–259. DOI:10.5201/ipol.2011.g_lmii
  • [10] Glassner AS. Graphics Gems. 1st ed., Academic Press Inc., 1993.
  • [11] Dhal KG, Das A, Ray S, Galvez J, Das S. Nature Inspired Optimization Algorithms and Their Application in Multi Thresholding Image Segmentation. Archives of Computational Methods in Engineering (2020) 27:855–888. DOI: 10.1007/s11831-019-09334-y [12] Otsu N. A threshold selection method from gray-level histograms. IEEE T Syst Man Cyb 1979; 9: 62–66. DOI: 10.1109/TSMC.1979.4310076
  • [13] Wagsta K, Cardie C, Rogers S, Schroedl S. .Constrained K-means Clustering with Background Knowledge. In: Proc. of the Eighteenth International Conference on Machine Learning (ICML 2001). 2001 ;1: 577-584.
  • [14] Dempster AP, Laird NM, Rubin DB. Maximum Likelihood from Incomplete Data via the EM Algorithm. J R Stat Soc 1977; 39: 1–38. DOI: 10.1111/j.2517-6161.1977.tb01600.x
  • [15] Wen Y, He L, Von Deneen KM, Lu Y. Brain tissue classification based on DTI using an improved Fuzzy C-means algorithm with spatial constraints. Magn Reson Imaging 2013; 31: 1623-1630. DOI: 10.1016/j.mri.2013.05.007
  • [16] Demirkaya O, Asyali MK, Sahoo PK. Image Processing with MATLAB: Application in Medicine and Biology. USA: CRC Press, 2009.
  • [17] Nguyen DMH, Vu HT, Ung HQ, Nguyen BT. 3D-brain segmentation using deep neural network and Gaussian mixture model. In Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV). 2017; 1: 815–824. DOI:10.1109/WACV.2017.96
  • [18] Huo Y, Xu Z, Xiong Y, Aboud K, Parvathaneni P, Bao S, Bermudez C, Resnick SM, Cutting LE, Landman BA. 3D whole brain segmentation using spatially localized atlas network tiles. NeuroImage 2019; 194: 105-119. DOI: 10.1016/j.neuroimage.2019.03.041
  • [19] Ramzan F, Khan MUG , Iqbal S, Saba T, Rehman A. Volumetric Segmentation of Brain Regions From MRI Scans Using 3D Convolutional Neural Networks. IEEE Access. 2020; 8:103697-103709. DOI:10.1109/ACCESS.2020.2998901
  • [20] Kong Y, Chen X, Wu J, Zhang P, Chen Y, Shu H. Automatic brain tissue segmentation based on graph filter. BMC Med Imaging. 2018; 18(1):9. DOI:10.1186/s12880-018-0252-x
  • [21] Kamarujjaman and Maitra M. 3D unsupervised modified spatial fuzzy c-means method for segmentation of 3D brain MR image. Pattern Anal Applic 2019; 22: 1561–1571. DOI:10.1007/s10044-019-00806-2
  • [22] Elaff I, El-Kemany A, Kholif M. Universal and stable medical image generation for tissue segmentation (The Unistable method). Turk J Electr Eng & Comp Sci 2017; 25: 1070-1081. DOI:10.3906/elk-1509-100.
  • [23] O’Donnell LJ, Westin CF. An Introduction to Diffusion Tensor Image Analysis. Neurosurgery Clinics of North America. 2011; 22(2):185-196. DOI: 10.1016/j.nec.2010.12.004
  • [24] Kingsley PB. Introduction to diffusion tensor imaging mathematics: Part I. Tensors, rotations, and eigenvectors. Concept Magn. Reson. 2006; 28:101–122. DOI: 10.1002/cmr.a.20048
  • [25] Basser PJ, Mattiello J, LeBihan D. MR Diffusion Tensor Spectroscopy and Imaging. Biophys J 1994; 66: 259-267. DOI: 10.1016/S0006-3495(94)80775-1
  • [26] Basser PJ, Pierpaoli C. Microstructural and Physiological Features of Tissues Elucidated by Quantitative-Diffusion-Tensor MRI. J Magn Reson 1996; 111: 209–219. DOI: 10.1006/jmrb.1996.0086
  • [27] Vilanova A, Zhang S, Kindlmann G, Laidlaw D. An Introduction to Visualization of Diffusion Tensor Imaging and Its Applications. In: Weickert, Joachim, Hagen, Hans. Visualization and Processing of Tensor Fields. Berlin Heidelberg: Springer, 2005. pp 121 – 153.
  • [28] Holtmannspotter M, Peters N, Opherk C, Martin D, Herzog J, Bruckmann H, Samann P, Gschwendtner A, Dichgans M. Diffusion Magnetic Resonance Histograms as a Surrogate Marker and Predictor of Disease Progression in CADASIL: A Two-Year Follow-Up Study. Stroke 2005; 36: 2559-2565. DOI: 10.1161/01.STR.0000189696.70989.a4.
  • [29] Seehaus A, Roebroeck A, Bastiani M, Fonseca L, Bratzke H, Lori N, Vilanova A, Goebel R, Galuske R. Histological validation of high-resolution DTI in human post mortem tissue. Front Neuroanat; 2015; 9:98. DOI: 10.3389/fnana.2015.00098
  • [30] Dyrby TB, Lundell H, Burke MW, Reislev NL, Paulson OB, Ptito M, Siebner HR. Interpolation of diffusion weighted imaging datasets. Neuroimage, 2014: 103:202–213. DOI: 10.1016/j.neuroimage.2014.09.005

Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)

Yıl 2023, Cilt: 4 Sayı: 2, 78 - 89, 21.12.2023

Ihab Elaff

https://doi.org/10.53525/jster.1250050

Öz

The 3D Uni-stable is a new method for 3D medical image enhancement which produces 3D Images of high contrast from the scanned anisotropic scaling images. This is done by estimating some intermediate slices through resizing the original scans. Rescaling has been achieved at three different levels: rescaling of eigenvalues of diffusion, rescaling the Scalar Indexes from the original eigenvalues, and rescaling the cluster maps of the segmentation of the original Scalar Indexes. Four interpolation methods have been employed at each level and four clustering algorithms have been employed in the process. The 3D Uni-stable image is almost universal as it combines variety of algorithms points of views into one 3D probability map. This reduces boundary-overlapping among different tissues, and hence improves the uniqueness of the segmentation problem solution. The stability factor of the 3D Uni-stable-Images is measured by maximum match analysis between the cluster maps which are generated from 3D Uni-stable images using variety of clustering methods with respect to true fact references for 5 different brains and the resultant standard deviations of Uni-stable images maximum match analysis in both threshold and tissue to brain ratio are much lower than Mean Diffusivity and Fractional Anisotropy scalar indexes for both CSF/non-CSF and WM/non-WM respectively

Anahtar Kelimeler

Uni-stable WM GM CSF Brain Segmentation

Kaynakça

  • [1] Doi K., Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics. 2017; 31(4):198-211. DOI: 10.1016/j.compmedimag.2007.02.002
  • [2] Li Q, Nishikawa RM, editors. Computer-Aided Detection and Diagnosis in Medical Imaging. Taylor & Francis, CRC Press, New York; 2015.
  • [3] Neubert A, Salvado O, Acosta O, Bourgeat P, Fripp J. Constrained reverse diffusion for thick slice interpolation of 3D volumetric MRI images. Computerized Medical Imaging and Graphics 2012; 36(2): 130-138. DOI: 10.1016/j.compmedimag.2011.08.004.
  • [4] Mori S. Introduction to Diffusion Tensor Imaging.1st ed., Oxford, UK: Elsevier, 2007.
  • [5] Liu T, Li H, Wong K, Tarokh A, Guo L, Wong S. Brain Tissue Segmentation Based on DTI Data. Neuroimage 2007; 38: 114-23. DOI: 10.1016/j.neuroimage.2007.07.002
  • [6] Zarei M, Johansen Berg H, Matthews PM. Diffusion Tensor Imaging and Tractography in Clinical Neurosciences. Iran J Radiol 2003; 1: 45-52.
  • [7] Khotanlou H, Colliot O, Atif J, Bloch I. 3D Brain Tumor Segmentation in MRI Using Fuzzy Classification, Symmetry Analysis and Spatially Constrained Deformable Models. Fuzzy Set Syst 2009; 160: 1457-1473. DOI:10.1016/j.fss.2008.11.016
  • [8] Dubey RB,Hanmandlu M,Gupta SK,Gupta SK. The Brain MR Image Segmentation Techniques and use of Diagnostic Packages. Acad Radiol. 2010; 17: 658 – 671. DOI:10.1016/j.acra.2009.12.017
  • [9] Getreuer P. Linear Methods for Image Interpolation, Image Processing On Line, 2011; 1:238–259. DOI:10.5201/ipol.2011.g_lmii
  • [10] Glassner AS. Graphics Gems. 1st ed., Academic Press Inc., 1993.
  • [11] Dhal KG, Das A, Ray S, Galvez J, Das S. Nature Inspired Optimization Algorithms and Their Application in Multi Thresholding Image Segmentation. Archives of Computational Methods in Engineering (2020) 27:855–888. DOI: 10.1007/s11831-019-09334-y [12] Otsu N. A threshold selection method from gray-level histograms. IEEE T Syst Man Cyb 1979; 9: 62–66. DOI: 10.1109/TSMC.1979.4310076
  • [13] Wagsta K, Cardie C, Rogers S, Schroedl S. .Constrained K-means Clustering with Background Knowledge. In: Proc. of the Eighteenth International Conference on Machine Learning (ICML 2001). 2001 ;1: 577-584.
  • [14] Dempster AP, Laird NM, Rubin DB. Maximum Likelihood from Incomplete Data via the EM Algorithm. J R Stat Soc 1977; 39: 1–38. DOI: 10.1111/j.2517-6161.1977.tb01600.x
  • [15] Wen Y, He L, Von Deneen KM, Lu Y. Brain tissue classification based on DTI using an improved Fuzzy C-means algorithm with spatial constraints. Magn Reson Imaging 2013; 31: 1623-1630. DOI: 10.1016/j.mri.2013.05.007
  • [16] Demirkaya O, Asyali MK, Sahoo PK. Image Processing with MATLAB: Application in Medicine and Biology. USA: CRC Press, 2009.
  • [17] Nguyen DMH, Vu HT, Ung HQ, Nguyen BT. 3D-brain segmentation using deep neural network and Gaussian mixture model. In Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV). 2017; 1: 815–824. DOI:10.1109/WACV.2017.96
  • [18] Huo Y, Xu Z, Xiong Y, Aboud K, Parvathaneni P, Bao S, Bermudez C, Resnick SM, Cutting LE, Landman BA. 3D whole brain segmentation using spatially localized atlas network tiles. NeuroImage 2019; 194: 105-119. DOI: 10.1016/j.neuroimage.2019.03.041
  • [19] Ramzan F, Khan MUG , Iqbal S, Saba T, Rehman A. Volumetric Segmentation of Brain Regions From MRI Scans Using 3D Convolutional Neural Networks. IEEE Access. 2020; 8:103697-103709. DOI:10.1109/ACCESS.2020.2998901
  • [20] Kong Y, Chen X, Wu J, Zhang P, Chen Y, Shu H. Automatic brain tissue segmentation based on graph filter. BMC Med Imaging. 2018; 18(1):9. DOI:10.1186/s12880-018-0252-x
  • [21] Kamarujjaman and Maitra M. 3D unsupervised modified spatial fuzzy c-means method for segmentation of 3D brain MR image. Pattern Anal Applic 2019; 22: 1561–1571. DOI:10.1007/s10044-019-00806-2
  • [22] Elaff I, El-Kemany A, Kholif M. Universal and stable medical image generation for tissue segmentation (The Unistable method). Turk J Electr Eng & Comp Sci 2017; 25: 1070-1081. DOI:10.3906/elk-1509-100.
  • [23] O’Donnell LJ, Westin CF. An Introduction to Diffusion Tensor Image Analysis. Neurosurgery Clinics of North America. 2011; 22(2):185-196. DOI: 10.1016/j.nec.2010.12.004
  • [24] Kingsley PB. Introduction to diffusion tensor imaging mathematics: Part I. Tensors, rotations, and eigenvectors. Concept Magn. Reson. 2006; 28:101–122. DOI: 10.1002/cmr.a.20048
  • [25] Basser PJ, Mattiello J, LeBihan D. MR Diffusion Tensor Spectroscopy and Imaging. Biophys J 1994; 66: 259-267. DOI: 10.1016/S0006-3495(94)80775-1
  • [26] Basser PJ, Pierpaoli C. Microstructural and Physiological Features of Tissues Elucidated by Quantitative-Diffusion-Tensor MRI. J Magn Reson 1996; 111: 209–219. DOI: 10.1006/jmrb.1996.0086
  • [27] Vilanova A, Zhang S, Kindlmann G, Laidlaw D. An Introduction to Visualization of Diffusion Tensor Imaging and Its Applications. In: Weickert, Joachim, Hagen, Hans. Visualization and Processing of Tensor Fields. Berlin Heidelberg: Springer, 2005. pp 121 – 153.
  • [28] Holtmannspotter M, Peters N, Opherk C, Martin D, Herzog J, Bruckmann H, Samann P, Gschwendtner A, Dichgans M. Diffusion Magnetic Resonance Histograms as a Surrogate Marker and Predictor of Disease Progression in CADASIL: A Two-Year Follow-Up Study. Stroke 2005; 36: 2559-2565. DOI: 10.1161/01.STR.0000189696.70989.a4.
  • [29] Seehaus A, Roebroeck A, Bastiani M, Fonseca L, Bratzke H, Lori N, Vilanova A, Goebel R, Galuske R. Histological validation of high-resolution DTI in human post mortem tissue. Front Neuroanat; 2015; 9:98. DOI: 10.3389/fnana.2015.00098
  • [30] Dyrby TB, Lundell H, Burke MW, Reislev NL, Paulson OB, Ptito M, Siebner HR. Interpolation of diffusion weighted imaging datasets. Neuroimage, 2014: 103:202–213. DOI: 10.1016/j.neuroimage.2014.09.005
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Ihab Elaff 0000-0002-0913-5476

Yayımlanma Tarihi 21 Aralık 2023
Gönderilme Tarihi 11 Şubat 2023
Kabul Tarihi 6 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 4 Sayı: 2

Kaynak Göster

APA Elaff, I. (2023). Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method). Journal of Science, Technology and Engineering Research, 4(2), 78-89. https://doi.org/10.53525/jster.1250050
AMA Elaff I. Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method). JSTER. Aralık 2023;4(2):78-89. doi:10.53525/jster.1250050
Chicago Elaff, Ihab. “Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)”. Journal of Science, Technology and Engineering Research 4, sy. 2 (Aralık 2023): 78-89. https://doi.org/10.53525/jster.1250050.
EndNote Elaff I (01 Aralık 2023) Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method). Journal of Science, Technology and Engineering Research 4 2 78–89.
IEEE I. Elaff, “Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)”, JSTER, c. 4, sy. 2, ss. 78–89, 2023, doi: 10.53525/jster.1250050.
ISNAD Elaff, Ihab. “Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)”. Journal of Science, Technology and Engineering Research 4/2 (Aralık 2023), 78-89. https://doi.org/10.53525/jster.1250050.
JAMA Elaff I. Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method). JSTER. 2023;4:78–89.
MLA Elaff, Ihab. “Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)”. Journal of Science, Technology and Engineering Research, c. 4, sy. 2, 2023, ss. 78-89, doi:10.53525/jster.1250050.
Vancouver Elaff I. Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method). JSTER. 2023;4(2):78-89.
 Kapak Resmi İndir
Dergide yayınlanan çalışmalar
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0) Uluslararası Lisansı ile lisanslanmıştır.
by-nc-nd.png

Free counters!