Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Dubey, R. B.
- Region Growing for MRI Brain Tumor Volume Analysis
Abstract Views :465 |
PDF Views:125
Authors
Affiliations
1 Dept. of E & IE, Apeejay College of Engg., Sohna, Gurgaon, IN
2 Dept. of EE, IIT, Delhi, IN
3 Vaish College of Engg., Rohtak, Haryana, IN
4 Dept. of EE, DCRUST, Murthal, Sonepat, Haryana, IN
1 Dept. of E & IE, Apeejay College of Engg., Sohna, Gurgaon, IN
2 Dept. of EE, IIT, Delhi, IN
3 Vaish College of Engg., Rohtak, Haryana, IN
4 Dept. of EE, DCRUST, Murthal, Sonepat, Haryana, IN
Source
Indian Journal of Science and Technology, Vol 2, No 9 (2009), Pagination: 26-31Abstract
The tumor volume is a significant prognostic factor in the treatment of malignant tumors. Manual segmentation of brain tumors from MR images is a challenging and time consuming task. A semi-automated region growing segmentation method is proposed to segment brain tumor from MR images. The proposed method can successfully segment a tumor provided that the parameters are set properly. This method is applied to 8-tumor contained MRI slices from 2 brain tumor patients' and satisfactory segmentation results are achieved.Keywords
Brain Tumor, MRI, Imaging, SegmentationFull Text
References
- Rafael C, Gonzalez and Richard Woods E (2003) Digital image processing. 2nd edition.
- Sonka M, Hlavac V and Boyle R (2003) Image processing, analysis and machine vision. 3rd edition.
- Edman M (2007) Segmentation using a region growing algorithm. Rensselaer Polytech. Instt.
- Haney SM, Thompson PM, Cloughesy TF, Alger JR and Toga AW (2001) Tracking tumor growth rates in patients with malignant gliomas: a test of two algorithms. AJNR Am. J. Neuroradiol. 22, 73-82.
- Mehnert and Jackway P (1997) An improved seeded region growing algorithm. Pattern Recog/ Letters. 18, 1065-1071.
- Basu S (1987) Image segmentation by semantic method. Pattern Recog. 20 (5) 497-511.
- Beaulieu JM and Goldberg M (1989) Hierarchy in picture segmentation: a stepwise optimisation approach. IEEE Trans. Pattern Analysis & Machine Intellig. 11 (2) 150-163.
- Gambotto JP (1993) A new approach to combining region growing and edge detection. Pattern Recog. Letters. 14, 869-875.
- Hojjatoleslami SA and Kittler J (2002) Region growing: a new approach. Dept. Electric. Electronics Engg., Univ. Surrey, Guildford, UK, GU25XH.
- Moonis et al. (2002) Estimation of tumor volume with fuzzy connectedness segmentation of MR images. ATRN Am. J. Neuroradiol. 23, 356-363.
- Liu J, Udupa JK, Odhner D, Hackney D and Moonis G (2005) A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Compu. Med. Imaging & Graphics. 29, 21-34.
- Lu Y, Jiang T and Zang Y (2003) Region growing method for the analysis of functional MRI data. NeuroImage. 20, 455-465.
- Salman YL, Assal MA, Badawi AM, Alian SM and MEI-EI Bayome (2005) Validation techniques for quantitative brain tumors measurements. IEEE Proc. Engg. Med. Biol. 7048-7051.
- Yang Y, Yan X, Zheng C and Lin P (2004) A novel statistical method for segmentation of brain MRI. IEEE, 946-949.
- Hwang J, Han Y and Park HW (2007) Segmentation of brain parenchyma using bilateral filtering and region growing. Intl. Conf. IEEE EMBS. 6263-6266.
- Vaidyanathan M, Clarke LP, Velthuizen RP, Phulphanich S, Bensaid AM, Hall LO, Bezdek JC, Greenberg H, Trotti A and Sibiger M (1995) Comparison of supervised MRI segmentation methods for volume determination during therapy. Magnetic Resonance Imaging. 13 (5), 719-728.
- Ma KL, Cohen MF and Painter JS (1991) Volume Seeds: A Volume exploration technique. J. Visual. Compu. Animation. 2, 135-140.
- Heath LM, Hall LO, Goldgof DB and Murtagh FR (2001) Automatic segmentation of non-enhancing brain tumors in magnetic resonace images. Artificial Intelligence in Med. 21, 43-63.
- Xie K, Yang J, Zau ZG and Zhu YM (2005) Semiautomatic brain tumor an edema segmentation using MRI. Eur. J. Radiol. 56 (1), 12-19.
- Khanpur RS (1999) Handbook of biomedical instrumentation. 2nd edition.
- Bu Lee, Soo-Min Song, Jae-Sung Lee and Myoung-Hee Kim (2005) Tumor segmentation from small animal PET using region growing based on gradient magnitude. Dept. of Computer Sci. Engg., Ewha Woman’s Univ.
- Level Set Detected Masses in Digital Mammograms
Abstract Views :373 |
PDF Views:104
Authors
Affiliations
1 Dept. of E & IE, Apeejay College of Engg., Sohna, Gurgaon
2 Dept. of EE, IIT, Delhi, IN
3 Vaish College of Engg, Rohtak, Haryana, IN
1 Dept. of E & IE, Apeejay College of Engg., Sohna, Gurgaon
2 Dept. of EE, IIT, Delhi, IN
3 Vaish College of Engg, Rohtak, Haryana, IN
Source
Indian Journal of Science and Technology, Vol 3, No 1 (2010), Pagination: 9-13Abstract
Breast cancer is the leading cause of death among woman. Currently X-ray mammography is the most widely used method for early detection of breast cancer. Many computer aided techniques are available to assist the radiologist in taking crucial decisions. A method uses level set for segmentation of masses in digital mammograms is introduced. This method uses the Gaussian filter for smoothing the image and noise reduction. Level set methods offer a powerful approach for the medical image segmentation since it can handle any of the cavities, concavities, convolution, splitting or merging. However, this method requires specifying initial curves and can only provide good results if these curves are placed near symmetrically with respect to the object boundary. The results of experimental study indicate that our scheme can provide useful contour extraction for mass structure.Keywords
Breast Cancer, Level Set, Mammograms, Gaussian FilterFull Text
References
- American cancer society (2004) Breast cancer facts and figure, ACS Web site.
- Bischoff S and Kobbelt L (2002) Isosurface reconstruction with topology control. Proceedings of Pacific Graphics, pp. 246–255.
- Bischoff S and Kobbelt L (2003) Sub-voxel topology control for level set surfaces. Proceedings of Eurographics, pp. 273–280.
- Bischoff S and Kobbelt L (2004) topologically correct extraction of the cortical surface of a brain using levelset methods. Proceedings of BVM.
- Boyle P and Ferly J (2005) Cancer incidence and morality in Europe. Ann. Oncology. 16(3), 481-488.
- Cardoso JS, Costa JFPD and Cardoso MJ (2005) SVMs applied to objective Aesthetic evaluation of conservative breast cancer treatment. IEEE Proceedings of Int. joint conf. on NN, pp.2481-2486.
- Chitre Y, Dhawan AP and Moskowtz M (1994) ANN based classification of mammography microclssification using image structure and cluster features. IEEE.
- Goldenberg R, Kimmel R, Rivlin E and Rudzsky M (2002) Cortex segmentation: A fast variational geometric approach. IEEE Transactions on Medical Imaging. 21(2), 1544–1551.
- Hanmandlu M, Vineel D and Singh G (2008) Computer aided fuzzy based technique for detection of suspicious masses and microclassifications from mammographic images. IICAI-07, pp.344-358.
- Hua LI, LElmoataz A, Fadilia J and Su R (2003) An improved image segmentation approach based on level set and mathematical morphology. Proceedingsof SPIE, 5286, 851.
- Kupinski MA and Giger M (1998) Automated seeded lesion segmentation on digital mammograms. IEEE Trans. Med. Imaging. 17(4), 510-517.
- Lefohn A, Cates J and Whitaker R (2003) Interactive, gpu-based level sets for 3d brain tumor segmentation. Proceedings of Medical Image computing andComputer Assisted Intervention (MICCAI).
- Leventon M (2000) Statistical Models for Medical Image Analysis. Ph.D. thesis, MIT.
- Malladi R and Sethian J (1996) Image processing: Flows under min/max curvature and mean curvature.Graphical Models and Image Processing, 58 (2) pp. 127-141.
- Malladi R, Sethian J and Vemuri BC (1995) Shape Modeling with Front Propagation: A Level Set Approach. IEEE Trans. PAMI, vol. 17, pp. 158-175.
- McInerney T and Terzopoulos D (1996) Deformable models in medical image analysis: A survey, Medical Image Analysis. 1(2), 91–108.
- Osher S and Fedkiw R (2003) Level Set Methods and Dynamic Implicit Surfaces. Springer Verlag, New York.
- Pham DL, Xu C and Prince JL (2000) Current methods in medical image segmentation. Ann. Rev. Biomed. Eng., vol. 2, 315-337.
- Preusser T and Rumpf M (2002) A level set method for anisotropic geometric diffusion in 3d image processing. SIAM J. Applied Mathematics, 62 (5) pp. 1772–1793.
- Rafael C, Gonzalez and Richard Woods E (2003) Digital Image Processing, 2nd edition.
- Sethian J (1982) An analysis of flame propagation, Ph.D. Thesis, University of California at Berkeley.
- Sethian JA (1996) Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics. Computer Vision and Material Science, Cambridge University, UK.
- Sonka M, Hlavac V and Boyle R (2007) Image Processing: Analysis and Machine Vision, 3rd edition.
- Suri JS, Liu KC, Singh S, Laxminarayana SN and Reden L (2002) Shape recovery algorithms using level sets for 2D and 3D medical imagery: A state-ofthe-art review. IEEE Trans. Info. Tech. in Biomed. 6(1), 8-28.
- Suri et al., (2001) Fast MR Brain Segmentation using Regional Level Sets. Intern. Jou. of Engg. Med. and Biol. 20(4), 84-95.
- Weeratunga S and Kamath C (2004) An investigation of implicit active contours for scientific image segmentation, Video Communications and Image Processing. SPIE Elec. Imaging. 5308, 210-221.
- Weeratunga S and Kamath C (2004) An investigation of implicit active contours for scientific image segmentation. Proc. of Visual Commun. & ImageProcessing Conf.
- Whitaker R, Breen D, Museth K and Soni N (2001) A framework for level set segmentation of volume datasets. Proc. Intl. Workshop on Volume Graphics. pp: 159–168.
- Whitaker R, Breen D, Museth K and Soni N (2001) segmentation of biological volume datasets using a level set framework. Proc. Intl. Workshop on Volume Graphics. pp: 249–263.