Image Segmentation of Cranial Vault for Clinical Analysis
Keywords:
Watershed, segmentation, cranial vault, MRI, LesionsAbstract
For several applications, segmented images gives better insight with increased accuracy and repeatability. Several segmentation algorithms were proposed for clinical purposes to diagnose, treatment and for tracking the progress of disease. Segmenting structures from medical images and reconstruction of specific anatomical shapes is difficult due to large size of datasets, complexity and variability of a given image. It is therefore, better to view the segmented images than the whole scan obtained from CT or MRI. Particularly, in surgical planning over diseased organ, segmented part is enough for visualization than the whole image. For example, if there is fracture in skull bone, it would be sufficient to view the fractured bone from a diagnostic image. Watershed segmentation is widely used in medical image processing applications because it is relatively fast in terms of computational time. An algorithm to segment the cranial vault bone based on Watershed method is presented. It is also implemented for few specific cranial vault abnormalities to demonstrate the results.
References
M. Stytz., Frieder, G. and Frieder, O. “Three - dimensional medical imaging: Algorithms and computer systems”. ACM Computing Surveys Vol. 23 Issue 4, 1991, pp.421–499.
N. Ayache. “ Medical computer vision, virtual reality and robotics”. Image and Vision Computing. Vol. 13, Iss. 4, 1995a , pp.295–313.
AS. Romer., and Parsons, T. S. “The Vertebrate Body”. Saunders College, Philadelphia, 1977.
DA. Wecht, Sawaya R. Lesions of the calvaia: “Surgical experience with 42 patients”. Ann. Surg. Oncol., Vol.4, 1997, pp. 28-36.
F. Hodges, “Pathology of the skull”. In. Taveras J, Ferrucci J. eds. Radiology: diagnosis, imaging and intervention. 2nd ed. Vol. 3, 1989, pp. 1-21.
D. Bastug, Ortiz O, Sydney S. Schochet. “Hemangiomas in the Calvaria: Imaging Findings”. AJR; Vol. 164, March 1995; pp.683-687.
NR. Pal and Pal SK. “A review on image segmentation techniques”. Pattern Recogn. Vol. 26, Issue-9, 1993, pp 1273-1294.
Pritee Gupta, Vandana Malik and Mallika Gandhi. “Implementation of Multilevel Threshold Method for Digital Images Used In Medical Image Processing”. International Journal of Advanced Research in Computer Science and Software Engineering, Vol.2, Issue-2, 2012.
Y.J.Zhang and J.J.Gerbrands, “Objective and quantitative segmentation evaluation and comparison”, Signal Processing 39, 1994, pp.43-54.
T. Law, Itoh H, Seki H. “Image filtering, edge detection, and edge tracing using fuzzy reasoning”. IEEE Trans PAMI. 1996;18, pp.481–91.
G. Chen, T. Hu, X. Guo, and X. Meng, "A fast region-based image segmentation based on least square method," in Proc. IEEE International Conference on Systems, Man and Cybernetics, SMC, 2009, pp.972-977.
N. Ramesh, Yoo JH, Sethi IK. “Thresholding based on histogram approximation”. IEEE Proc Vision Image Signal Proc. 1995; 142, pp.271–9.
S. Zhu, X. Xia, Q. Zhang, and K. Belloulata, "An image segmentation algorithm in image processing based on threshold segmentation," in Proc. Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, SITIS'0., 2007, pp. 673-678.
Fitz Simmons, Ellen; Jack H. Prost & Sharon Peniston . "Infant Head Molding, A Cultural Practice,"Arch. Fam. Med., 7 (January/February) 1998.
L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, 1991, pp. 583–598.
L. Vincent, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms,” IEEE Transactions on Image Processing, vol. 2, no. 2, 1993, pp. 176–201.
Wafa Abid Fourati and Mohamed Salim Bouhlel, “Trabecular Bone Image Segmentation Using Wavelet and Marker-Controlled Watershed Transformation,” Chinese Journal of Engineering, 2014.
S. Ravi, Khan AM. “Bio-Medical Image Segmentation Using Marker Controlled Watershed Algorithm: A Case Study”. IJRET, Vol 3, Spl. Issue- 3, May 2014, pp 26-30.
H. Digabel and C. Lantuéjoul, “Iterative algorithms,” in Proceedings of the 2nd European Symposium on Quantitative Analysis of Microstructures in Materials Sciences, Biology and Medicine, 1977, pp. 85–99.
Lázaro Amaral; Miriam Chiurciu; João Ricardo Almeida; Nelson Fortes Ferreira; Renato Mendonça; Sérgio Santos Lima. “MR imaging for evaluation of lesions of the cranial vault: a pictorial essay”. Arq. Neuro-Psiquiatr. vol.61 Issue-.3A São Paulo Sept. 2003.
P K Nayak M Ch, A K Mahapatra M Ch.” Primary reconstruction of depressed skull fracture - The changing scenario”. Indian Journal of Neurotrauma (IJNT) 2007, Vol. 5, No. 1, pp. 35-38.
M. Seruya, Tan SY, Wray AC, Penington AJ, Greensmith AL, Holmes AD, Chong DK. “Total cranial vault remodeling for isolated sagittal synostosis: part I. Postoperative cranial suture patency”. Plast Reconstr Surg. 2013 Oct; 132(4): pp.602e-610e
J. Rajiv. Iyengar, BS, Petra M. Klinge, Wendy Chen, Stephen R. Sullivan, Helena O. Taylor, “Management of craniosynostosis at an advanced age: Clinical findings and interdisciplinary treatment in a 17 year-old with pan-suture synostosis”. Interdisciplinary Neurosurgery. Vol. 2, Iss.1, 2015, pp. 61-64.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
