Multimedia Tools and Applications

, Volume 76, Issue 3, pp 3809–3828 | Cite as

Statistical textural feature and deformable model based brain tumor segmentation and volume estimation



Segmentation and precise volume estimation of abnormalities is one of the main focus in medical image processing field for the purpose of diagnosis and treatment planning. The precise estimation of volume of the abnormality aids better prognosis, treatment planning and dose estimation. The work put forth in this paper has proposed and implemented a semi-automatic technique that yields appropriate segmented regions from MR brain images. The Segmentation technique here utilizes fusion of information beyond human perception from MR images to develop a fused feature map. The information beyond human perception include second order derivatives that are computed from an image which are discussed in detail in relevant section of this paper. This obtained feature map acts as a stopping function for the initialized curve in the framework of an active contour model to obtain a well segmented region of interest. The segmentation is carried out in all the slices of a particular dataset with initialization of the active contour required only on the first slice which makes this method fast. The obtained segmentation results are compared with ground truth segmentation results obtained from experts manually using Jackard’s Co-efficient of Similarity and Overlap index. The boundaries of the segmented regions are utilized in surveyor’s algorithm to compute the volume of the tumors with high accuracy. The efficacy of this volume estimation technique is illustrated with comparison to mostly used ABC/2 method and cavalieri method. The results obtained on various case studies like Craniophryngioma, High grade Glioma and Microadenoma show a good efficacy of the overall method.


Magnetic Resonance Imaging (MRI) Segmentation Gray Level Co-occurrence Matrix (GLCM) Gray Level Run length Matrix (GLRLM) Volume estimation Jackard’s similarity Index (JSI) Overlap Index (OI) Active Contour Model (ACM) Principle Component Analysis (PCA) 


  1. 1.
    Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkCrossRefMATHGoogle Scholar
  2. 2.
    Bilgic S, Sahin B, Sonmez OF, Odaci E, Colakoglu S, Kaplan S et al (2005) A new approach for the estimation of intervertebral disc volume using the cavalieri principle and computed tomography images. Clin Nuerol Neurosurg 107:282–288CrossRefGoogle Scholar
  3. 3.
    Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79CrossRefMATHGoogle Scholar
  4. 4.
    Chakraborty A, Staib LH, Duncan JS (1996) Deformable boundary finding in medical images by integrating gradient and region information. IEEE Trans Med Imag 15(6):859–870CrossRefGoogle Scholar
  5. 5.
    Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277CrossRefMATHGoogle Scholar
  6. 6.
    Clausi DA, Deng H (2005) Design-based texture feature fusion using Gabor filters and co-occurrenceprobabilities. IEEETrans Image Process 14(7):925–936CrossRefGoogle Scholar
  7. 7.
    Clausi DA, Deng H (2005) Design-based texture feature fusion using Gabor filters and co- occurrence probabilities. IEEE Trans Image Process 14(7):925–936CrossRefGoogle Scholar
  8. 8.
    Coleman GB, Andrews HC (1979) Image segmentation by clustering. Proc IEEE 67(5):773–785CrossRefGoogle Scholar
  9. 9.
    Cremers D, Rousson M, Deriche R (2007) A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int J Comput Vis 72(2):195–215CrossRefGoogle Scholar
  10. 10.
    del Fresno M, Venere M, Clausse A (2009) A combined region growing and deformable model method for extraction of closed surfaces in 3D CT and MRI scans. Comput Med Imaging Graph 33(5):369–376CrossRefGoogle Scholar
  11. 11.
    Dou WB, Ruan S, Chen YP, Bloyet D, Constans JM (2007) A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images. Image Vis Comput 25(2):164–171CrossRefGoogle Scholar
  12. 12.
    Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New YorkMATHGoogle Scholar
  13. 13.
    Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybernet: Trans Am Soc Cybernet 3(3):32–57MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Gabel JM, Sila CA, Sloan MA et al (1998) comparison of ABC/2 estimation technique to computer assisted volumetric analysis of intraparenchymal and subdural hematomas complicating the GUSTO-1 trial. Stroke 29:1799–1801CrossRefGoogle Scholar
  15. 15.
    Gebel JM, Sila CA, Sloan MA, Granger CB, Weisenberger JP, Green CL (1998) Comparison of the ABC/2 estimation technique to computer-assisted volumet- ric analysis of intraparenchymal and subdural hematomas complicating the gusto-1 trial. Stroke 29:1799–1801CrossRefGoogle Scholar
  16. 16.
    Haffner JPE, Bouyé S, Puech P, Leroy X, Lemaitre L, Villers A (2009) Peripheral zone prostate cancers: location and intraprostatic patterns of spread at histopathology. Prostate 69(3):276–282CrossRefGoogle Scholar
  17. 17.
    Hansasuta A, Choi CY, Gibbs IC, Soltys SG, Tse VC, Lieberson RE et al (2011) Multisession stereotactic radiosurgery for vestibular schwanno- mas: single-institution experience with 383 cases. Neurosurgery 69:1200–1209CrossRefGoogle Scholar
  18. 18.
    Haralick RM () A texturecontext feature extraction algorithm 1241, Mar. 1973. for remotely sensed imagery. Roc I971 I1971EEE Decisim Confrol Conf. (Gainde, FL), 650–657Google Scholar
  19. 19.
    Huttner HB, Steiner T, Hartman M et al (2006) Comparison of ABC/2 estimation technique to computer assisted planimetric analysis in warafin related intracerebral parenchymal hemorrhage. Stroke 37:404–408CrossRefGoogle Scholar
  20. 20.
    Jayadevappa D, Srinivas Kumar S, Murty DS (2011) Medical image segmentation algorithms using deformable models: a review. IETE Tech Rev 28(3)Google Scholar
  21. 21.
    Kapur T, Eric W, Grimson L, Wells WM III, Kikinis R (1996) Segmentation of brain tissue from magnetic resonance images. Med Image Anal 1(2):109–127CrossRefGoogle Scholar
  22. 22.
    Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331CrossRefMATHGoogle Scholar
  23. 23.
    Kichenassamy S, Kumar A, Olver P, Tannenbaum A, Yezzy A (1995) “Gradient flows and geometric active contour models,”. 22 Comput Math Methods Med Proc 5th Int Conf Comput Vision (ICCV’95), 810–815Google Scholar
  24. 24.
    Kothari RU, Brott T, Broderick JP, Barson WG, Sauerbeck LR, Zuccarello M et al (1996) The ABCs of measuring intracerebral hemorrhage volumes. Stroke 27:1304–1305CrossRefGoogle Scholar
  25. 25.
    Li BN, Chui CK, Chang S, Ong SH (2011) Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Comput Biol Med 41(1):1–10CrossRefGoogle Scholar
  26. 26.
    Li B, Huang DS (2008) Locally linear discriminant embedding: an efficient method for face recognition. Pattern Recogn 41(12):3813–3821CrossRefMATHGoogle Scholar
  27. 27.
    Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN, Gore JC (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007–2016MathSciNetCrossRefGoogle Scholar
  28. 28.
    Liew AW-C, Yan H (2006) Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images. Curr Med Imag Rev 2(1):91–103CrossRefGoogle Scholar
  29. 29.
    Logeswari T, Karnan M (2010) An improved implementation of brain tumor detection using segmentation based on hierarchical self-organizing map. Int J Comput Theory Eng 2(4):1793–8201, LONI (2014) Google Scholar
  30. 30.
    Luby M, Hong J, Merino JG, Lynch JK, Hsia AW, Magadan A, Song SS, Latour LL, Warach S Stroke mismatch volume with the use of ABC/2 is equivalent to planimetric stroke mismatch volume. J Nucl Med, AJNR Am J Neuroradiol 34; 1901–07Google Scholar
  31. 31.
    Magi-Galluzzi C et al (2011) International Society of Urological Pathology (ISUP)Consensus Conference on Handling and Staging of Radical Prostatectomy Specimens. Working group 3: extraprostatic extension, lymphovascular invasion and locally advanced disease. Modern Pathol : Off J United States Can Acad Pathol, Inc 24(1):26–33CrossRefGoogle Scholar
  32. 32.
    Malladi R, Sethian JA, Vemuri BC (1995) Shape modelling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175CrossRefGoogle Scholar
  33. 33.
    Massick DD, Welling DB, Dodson EE, Scholfield M, Nagaraja HN, Schmalbrock P et al (2000) Tumor growth and audiometric change in vestibular schwannomas managed conservatively. Laryngoscope 110:1843–1849CrossRefGoogle Scholar
  34. 34.
    Masutani Y, Schiemann T, Hohne KH (1998) Vascular shape segmentation and structure extraction using a shape based region-growing model. Medical Image Computing and Computer-Assisted Interventation—MICCAI'98. Proceedings of the 1st International Conference Cambridge, MA, USA, October 11–13, 1998, vol 1496 of Lecture Notes in Computer Science, pp. 1242–1249, Springer, Berlin, GermanyGoogle Scholar
  35. 35.
    Mir AH, Hanmandlu M, Tandon SN (1995) Texture analysis of CT images. Eng Med Biol Mag, IEEE 14(6):781–786. doi:10.1109/51.473275 CrossRefGoogle Scholar
  36. 36.
    Montironi R et al (2003) Handling and pathology reporting of radical prostatectomy specimens. Eur Urol 44(6):626–636CrossRefGoogle Scholar
  37. 37.
    Olabaoriago SD, Smeulders AWM (2001) Interaction in the segmentation of medical image analysis. Nuero Image 5:127–142Google Scholar
  38. 38.
    Ortiz A, Gorriz JM, Ramirez J, Salas-Gonzalez D (2014) Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering. Inf Sci 262:117–136. doi:10.1016/j.ins.2013.10.002 CrossRefGoogle Scholar
  39. 39.
    Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comp Phys 79:12–49MathSciNetCrossRefMATHGoogle Scholar
  40. 40.
    Passat N, Ronse C, Baruthio J, Armspach J-P, Maillot C, Jahn C (2005) Region-growing segmentation of brain vessels: an atlas-based automatic approach. J Magn Reson Imaging 21(6):715–725CrossRefGoogle Scholar
  41. 41.
    Pham DL, Xu CY, Prince JL (2000) A survey of current methods in medical image segmentation. Ann Rev Biomed Eng 2:315–337 [Technical report version, JHU/ECE 99–01, Johns Hopkins University] CrossRefGoogle Scholar
  42. 42.
    Pitas L (1993) Digital image processing algorithms. Pentice HallGoogle Scholar
  43. 43.
    Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imag 13(1):146–168CrossRefGoogle Scholar
  44. 44.
    Shang L, Huang DS, Du JX, Zheng CH (2006) Palm-print recognition using fast ICA algorithm and radial basis probabilistic neural network. Neuro-computing 69(13–15):1782–1786Google Scholar
  45. 45.
    Solberg AHS, Jain AK (1997) Texture fusion and feature selection applied to SAR imagery. IEEE Trans Geosci Remote Sens 35(2):475–479CrossRefGoogle Scholar
  46. 46.
    Unal B, Kara A, Aksak S, Unal D (2010) A stereological assessment method for estimating the surface area of cycloids. T Eurasian J Med 42:66–73CrossRefGoogle Scholar
  47. 47.
    Van der Kwast TH et al (2011) International Society of Urological Pathology (ISUP) Consensus Conference on Handling and Staging of Radical Prostatectomy Specimens. working group 2: T2 substaging and prostate cancer volume. Modern Pathol : Off J United States Can Acad Pathol, Inc 24(1):16–25CrossRefGoogle Scholar
  48. 48.
    Wang J, Kong J, Lu Y, Qi M, Zhang B (2008) A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints. Comput Med Imaging Graph 32(8):685–698. doi:10.1016/j.compmedimag.2008.08.004, ISSN 0895–6111 CrossRefGoogle Scholar
  49. 49.
    Warfield SK, Kaus M, Jolesz FA, Kikinis R (2000) Adaptive, template moderated, spatially varying statistical classification. Med Image Anal 4(1):43–55CrossRefGoogle Scholar
  50. 50.
    Weglinski T, Fabijanska A (201) Brain tumor segmentation from MRI data sets using region growing approach. Proc 7th Int Confe Perspect Technol Methods MEMS Design (MEMSTECH’11), 185–188Google Scholar
  51. 51.
    Wells WM III, Crimson WEL, Kikinis R, Jolesz FA (1996) Adaptive segmentation of MRI data. IEEE Trans Med Imaging 15(4):429–442CrossRefGoogle Scholar
  52. 52.
    Xu C, Prince JL (Mar. 1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369Google Scholar
  53. 53.
    Xue J-H, Pizurica A, Philips W, Kerre E, van de Walle R, Lemahieu I (2003) An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images. Pattern Recogn Lett 24(15):2549–2560CrossRefGoogle Scholar
  54. 54.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353MathSciNetCrossRefMATHGoogle Scholar
  55. 55.
    Zhang N, Ruan S, Lebonvallet S, Liao Q, Zhu Y (2011) Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Comput Vis Image Underst 115(2):256–269, ISSN 1077–3142 CrossRefGoogle Scholar
  56. 56.
    Zhao ZQ, Huang DS, Sun BY (2004) Human face recognition based on multiple features using neural networks committee. Pattern Recognit Lett 25(12):1351–1358CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of TechnologySrinagarIndia

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