An Advanced technique for volumetric analysis

An accurate segmentation is critical, especially when the tumor morphological changes remain subtle, irregular and difficult to assess by clinical examination. This quantitative measurement depends on the accuracy of the segmentation method used. The undesired partial volume effect, which lies on a boundary between a high intensity region and low intensity region, makes unerring boundary determination a difficult task. A new approach to segmentation is proposed that removes the adverse effect on the boundary, which is unwanted especially from the point of view of volume rendering. This approach gives more accurate boundary detection and holes filling after segmentation. A semi-automatic calculation of volumetric size of brain tumor has been implemented in this approach. A comparative analysis of manual, seeded region growing and this advance approach shows more accurate and better performance for 3D volume measurements. This method is tested by two patients of different tumor type and shape, and better results are reported.

[1]  A Horsman,et al.  Tumour volume determination from MR images by morphological segmentation , 1996, Physics in medicine and biology.

[2]  Mie Sato,et al.  A gradient magnitude based region growing algorithm for accurate segmentation , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[3]  M Goldsmith,et al.  NUCLEAR MAGNETIC RESONANCE AS A NEW TOOL IN CANCER RESEARCH: HUMAN TUMORS BY NMR , 1973, Annals of the New York Academy of Sciences.

[4]  H. Soltanian-Zadeh,et al.  Optimal linear transformation for MRI feature extraction , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[5]  Isabelle Bloch,et al.  3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models , 2009, Fuzzy Sets Syst..

[6]  Paul Thompson,et al.  Mapping tumor growth rates in patients with malignant gliomas: A test of two algorithms , 2000, NeuroImage.

[7]  Bram van Ginneken,et al.  Toward automated segmentation of the pathological lung in CT , 2005, IEEE Transactions on Medical Imaging.

[8]  Hamid Soltanian-Zadeh,et al.  A comparative analysis of several transformations for enhancement and segmentation of magnetic resonance image scene sequences , 1992, IEEE Trans. Medical Imaging.

[9]  HyunWook Park,et al.  Segmentation of Brain Parenchyma using Bilateral Filtering and Region Growing , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Yingli Lu,et al.  A split–merge‐based region‐growing method for fMRI activation detection , 2004, Human brain mapping.

[11]  Jing Zheng,et al.  Fractal-based brain tumor detection in multimodal MRI , 2009, Appl. Math. Comput..

[12]  Yalin Zheng,et al.  Automated segmentation of lumbar vertebrae in digital videofluoroscopic images , 2004, IEEE Transactions on Medical Imaging.

[13]  Qingmin Liao,et al.  Statistical Structure Analysis in MRI Brain Tumor Segmentation , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[14]  R. Velthuizen,et al.  Technical aspects and evaluation methodology for the application of two automated brain MRI tumor segmentation methods in radiation therapy planning. , 2006, Magnetic resonance imaging.

[15]  J. Woolford.  ‘Fa’ , 2020, Rodgers and Hammerstein’s The Sound of Music.

[16]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[17]  Julie Delon,et al.  DETECTION OF GLIOMA EVOLUTION ON LONGITUDINAL MRI STUDIES , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[18]  Dewey Odhner,et al.  A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[19]  R. Velthuizen,et al.  Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. , 2004, International journal of radiation oncology, biology, physics.

[20]  L O Hall,et al.  Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.

[21]  Lawrence O. Hall,et al.  Automatic tumor segmentation using knowledge-based techniques , 1998, IEEE Transactions on Medical Imaging.

[22]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[23]  Forrest W. Crawford,et al.  Semi-automated segmentation of brain tumor lesions in MR Images. , 2006 .

[24]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[25]  R P Velthuizen,et al.  MRI segmentation: methods and applications. , 1995, Magnetic resonance imaging.

[26]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[27]  Lin Shen,et al.  Automatic Segmentation of the Caudate Nucleus From Human Brain MR Images , 2007, IEEE Transactions on Medical Imaging.

[28]  D. Kennedy,et al.  Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging. , 1989, IEEE transactions on medical imaging.

[29]  Jie Yang,et al.  Semi-automated brain tumor and edema segmentation using MRI. , 2005, European journal of radiology.

[30]  J. Udupa,et al.  Estimation of tumor volume with fuzzy-connectedness segmentation of MR images. , 2002, AJNR. American journal of neuroradiology.

[31]  L G Nyúl,et al.  On standardizing the MR image intensity scale , 1999, Magnetic resonance in medicine.

[32]  Peter Gluchowski,et al.  F , 1934, The Herodotus Encyclopedia.

[33]  Hideki Yoshikawa,et al.  Automated segmentation of acetabulum and femoral head from 3-d CT images , 2003, IEEE Transactions on Information Technology in Biomedicine.

[34]  Matt Edman.  Segmentation Using a Region Growing Algorithm , 2007, The Insight Journal.

[35]  Bulletin électronique.  EB , 2020, Catalysis from A to Z.

[36]  PÄ , 2019, Springer Reference Medizin.

[37]  Jian Pei.  JK , 2019, Springer Reference Medizin.

[38]  Jean-Marc Constans,et al.  A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images , 2007, Image Vis. Comput..

[39]  K. Zou,et al.  Three validation metrics for automated probabilistic image segmentation of brain tumours , 2004, Statistics in medicine.