Soft Computing

, Volume 23, Issue 19, pp 9265–9286 | Cite as

New level set approach based on Parzen estimation for stroke segmentation in skull CT images

  • Elizângela de S. Rebouças
  • Regis C. P. Marques
  • Alan M. Braga
  • Saulo A. F. Oliveira
  • Victor Hugo C. de AlbuquerqueEmail author
  • Pedro P. Rebouças Filho


Stroke is the second most common cause of death and one of the leading causes of disability in industrialized countries. Among the 17.5 million deaths caused by cardiovascular disease in 2012, approximately 6.7 million were caused by stroke. This study is focused on the hemorrhagic type of stroke, which accounts for 40% of all stroke deaths. This work proposes a new approach to segment the stroke from cranial CT images, in order to aid medical diagnosis. This approach proposes to automatically start the level set method within the stroke region and to use a nonparametric estimation approach based on the Parzen window to segment the stroke. The results obtained by the proposed approach are compared with the results of the level set algorithms using fuzzy C-means, and the level set based on the method of coherent propagation, fuzzy C-means, Watershed and Region Growth, which are commonly used in this field. The assessment is based on the validation of the segmentation from a radiologist. The experimental results showed that the proposed method presented a superior performance compared to the other commonly used methods, thus indicating that it is a promising tool for medical diagnosis. The results show that the proposed method has the highest mean of accuracy with 99.84% and lowest standard deviation of 0.08%, demonstrating that the proposed method is superior to the others in the literature. These results are confirmed by the high indexes of accuracy, sensitivity and specificity.


Stroke region segmentation Parzen window Level set Aid to medical diagnosis 



The authors acknowledge the financial support and encouragement from the Brazilian National Council for Research and Development (CNPq). The authors thank the Graduate Program in Computer Science from the Instituto Federal do Ceará and the Department of Computer Engineering and Walter Cantídio University Hospital of Universidade Federal do Ceara for technical support in Pulmonology and images. The last author acknowledges the sponsorship from the Federal Institute of Education, Science and Technology of Ceara via Grants PROINFRA/2017 and PROINFRA PPG/2017.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Abdel-Basset M, Fakhry AE, El-Henawy I, Qiu T, Sangaiah AK (2017) Feature and intensity based medical image registration using particle swarm optimization. J Med Syst 41(12):197Google Scholar
  2. Aicha B, Abdelhafid B (2012) Morphological segmentation of the spleen from abdominal CT images. Int J Image Graph Signal Process 4(4):56Google Scholar
  3. Al-Faris AQ, Ngah UK, Isa NAM, Shuaib IL (2014) Breast MRI tumour segmentation using modified automatic seeded region growing based on particle swarm optimization image clustering. In: Snášel V, Krömer P, Köppen M, Schaefer G (eds) Soft computing in industrial applications. Springer International Publishing, Cham, pp 49–60Google Scholar
  4. Almeida JS, Marinho LB, Souza JWM, Assis EA, Reboucas Filho PP (2018) Localization system for autonomous mobile robots using machine learning methods and omnidirectional sonar. IEEE Latin Am Trans 16(2):368–374Google Scholar
  5. Beucher S, Lantuéjoul C (1979) Use of watersheds in contour detection. In: International conference on image processing, Rennes, França, 1979. Societá Astronómica Italiana, pp 1–10Google Scholar
  6. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, NorwellzbMATHGoogle Scholar
  7. Bhadauria HS, Singh A, Dewal ML (2013) An integrated method for hemorrhage segmentation from brain CT imaging. Comput Electr Eng 39(5):1527–1536Google Scholar
  8. Bhadauria NS, Bist MS, Patel RB, Bhadauria HS (2015) Performance evaluation of segmentation methods for brain ct images based hemorrhage detection. In: 2015 2nd International conference on computing for sustainable global development (INDIACOM), New Delhi, India, March 2015. Bharati Vidyapeeths Inst Comp Applicat & Management; IEEE Delhi Sect, pp 1955–1959Google Scholar
  9. Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, SecaucuszbMATHGoogle Scholar
  10. Caselles V, Sapiro G, Chung DH (2000) Vector median filters, vector morphology, and coupled PDE’s: Theoretical connections. J Math Imaging Vis 12:109–120zbMATHGoogle Scholar
  11. Chien S-Y, Huang Y-W, Chen L-G (2003) Predictive watershed: a fast watershed algorithm for video segmentation. IEEE Trans Circuits Syst Video Technol 13(5):453–461Google Scholar
  12. Chuang K-S, Tzeng H-L, Chen S, Wu J, Chen T-J (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15Google Scholar
  13. da Silva Felix JH, Cortez PC, Rebouças Filho PP, de Alexandria AR, Costa RCS, Holanda MA (2008) Identification and quantification of pulmonary emphysema through pseudocolors. In: Mexican international conference on artificial intelligence. Springer, pp 957–964Google Scholar
  14. Dalca AV, Sridharan R, Cloonan L, Fitzpatrick KM, Kanakis A, Furie KL, Rosand J, Wu O, Sabuncu M, Rost NS et al (2014) Segmentation of cerebrovascular pathologies in stroke patients with spatial and shape priors. In: Medical image computing and computer-assisted intervention (MICCAI 2014). Springer, Boston, MA, USA, Sep, pp 773–780Google Scholar
  15. de Albuquerque VHC, Rebouças Filho PP, Cavalcante TS, Tavares JMRS (2010) New computational solution to quantify synthetic material porosity from optical microscopic images. J Microsc 240(1):50–59MathSciNetGoogle Scholar
  16. de Albuquerque VHC, Damaševičius R, Garcia NM, Pinheiro PR, Pedro Filho PR (2017) Brain computer interface systems for neurorobotics: methods and application. BioMed Res Int 2017:1–2Google Scholar
  17. de Souza JWM, Alves SSA, de Rebouças ES, Almeida JS, Rebouças Filho PP (2018) A new approach to diagnose parkinson’s disease using a structural cooccurrence matrix for a similarity analysis. Comput Intell Neurosci 2018:1–8Google Scholar
  18. Gillebert CR, Humphreys GW, Mantini D (2014) Automated delineation of stroke lesions using brain CT images. NeuroImage Clin 4:540–548Google Scholar
  19. Gomes SL, de Rebouças ES, Neto EC, Papa JP, de Albuquerque VHC, Rebouças Filho PP, Tavares JMRS (2017) Embedded real-time speed limit sign recognition using image processing and machine learning techniques. Neural Comput Appl 28(1):573–584Google Scholar
  20. Gonzalez RC, Woods RE (2010) Processamento Digital de Imagens, 3rd edn. Pearson Prentice Hall, São PauloGoogle Scholar
  21. Grau V, Mewes AUJ, Alcaniz M, Kikinis R, Warfield SK (2004) Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 23(4):447–458Google Scholar
  22. Holanda GB, Souza JWM, Lima DA, Marinho LB, Girão AM, Frota JBB, Rebouças Filho PP (2018) Development of OCR system on android platforms to aid reading with a refreshable braille display in real time. Measurement 120:150–168Google Scholar
  23. Jaccard P (1901) Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Impr, CorbazGoogle Scholar
  24. Kavitha AR, Chellamuthu C (2013) Detection of brain tumour from MRI image using modified region growing and neural network. Imaging Sci J 61(7):556–567Google Scholar
  25. Kimia BB, Tannenbaum AR, Zucker SW (1995) Shapes, shocks, and deformations I: the components of two-dimensional shape and the reaction–diffusion space. Int J Comput Vis 15(3):189–224Google Scholar
  26. Lee Y, Takahashi N, Tsai D-Y, Fujita H (2006) Detectability improvement of early sign of acute stroke on brain CT images using an adaptive partial smoothing filter. In: Medical Imaging, San Diego, California, 2006. International Society for Optics and Photonics, pp 61446Q–61446QGoogle Scholar
  27. Li C, Kao C-Y, Gore JC, Ding Z (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10):1940–1949MathSciNetzbMATHGoogle Scholar
  28. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Abraham J, Adair T, Aggarwal R, Ahn SY (2013) Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. Lancet 380(9859):2095–2128Google Scholar
  29. Maier O, Wilms M, von der Gablentz J, Krämer UM, Münte TF, Handels H (2015) Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. J Neurosci Methods 240:89–100Google Scholar
  30. Marinho LB, Almeida JS, Souza JWM, Albuquerque VHC, Rebouças Filho PP (2017) A novel mobile robot localization approach based on topological maps using classification with reject option in omnidirectional images. Exp Syst Appl 72:1–17Google Scholar
  31. Marinho LB, Rebouças Filho PP, Almeida JS, Souza JWM, Junior AHS, de Albuquerque VHC (2018) A novel mobile robot localization approach based on classification with rejection option using computer vision. Comput Electr Eng 68:26–43Google Scholar
  32. Matesin M, Loncaric S, Petravic D (2001) A rule-based approach to stroke lesion analysis from CT brain images. In: ISPA 2001: Proceedings of the 2nd international symposium on image and signal processing and analysis, Pula, Croatia, 2001. IEEE Reg 8; EURASIP, pp 219–223Google Scholar
  33. Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta (BBA) Protein Struct 405(2):442–451Google Scholar
  34. Mendis S, Armstrong T, Bettcher D, Branca F, Lauer J, Mace C, Poznyak V, Riley L, Silva VDCE, Stevens G (2014) Global status report on noncommunicable diseases 2014. World Health Organization, Geneva, pp 14–15Google Scholar
  35. Messay T, Hardie RC, Rogers SK (2010) A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 14(3):390–406Google Scholar
  36. Mitiche A, Ayed IB (2010) Variational and level set methods in image segmentation. Springer, BerlinzbMATHGoogle Scholar
  37. Moreira FDL, Kleinberg MN, Arruda HF, Freitas FNC, Parente MMV, de Albuquerque VHC, Rebouças Filho PP (2016) A novel vickers hardness measurement technique based on adaptive balloon active contour method. Exp Syst Appl 45:294–306Google Scholar
  38. Naidich TP, Castillo M, Cha S, Smirniotopoulos JG (2012) Imaging of the brain: expert radiology series, 1st edn. Elsevier Health Sciences, PhiladelphiaGoogle Scholar
  39. Neto EC, Cortez PC, Cavalcante TS, da Silva Filho VER, Rebouças Filho PP, Holanda MA (2015a) Supervised enhancement filter applied to fissure detection. In: VI Latin American Congress on biomedical engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. Springer, pp 337–340Google Scholar
  40. Neto EC, Reboucas ES, de Moraes JL, Gomes SL, Reboucas Filho PP (2015b) Development control parking access using techniques digital image processing and applied computational intelligence. IEEE Latin Am Trans 13(1):272–276Google Scholar
  41. Neto EC, Gomes SL, Rebouças Filho PP, de Albuquerque VHC (2015c) Brazilian vehicle identification using a new embedded plate recognition system. Measurement 70:36–46Google Scholar
  42. Neto EC, Cortez PC, Cavalcante TS, Rodrigues VE, Reboucas Filho PP, Holanda MA (2016) 3D lung fissure segmentation in TC images based in textures. IEEE Latin Am Trans 14(1):254–258Google Scholar
  43. Peng F, Yuan K, Feng S, Chen W (2008) Region feature extraction of brain ct image for classification. In: 2nd International conference on bioinformatics and biomedical engineering (ICBBE), Shanghai, May 2008. Wuhan University, pp 2495–2498Google Scholar
  44. Przelaskowski A, Sklinda K, Bargieł P, Walecki J, Biesiadko-Matuszewska M, Kazubek M (2007) Improved early stroke detection: wavelet-based perception enhancement of computerized tomography exams. Comput Biol Med 37(4):524–533Google Scholar
  45. Rajini NH, Bhavani R (2013) Computer aided detection of ischemic stroke using segmentation and texture features. Measurement 46(6):1865–1874Google Scholar
  46. Ramalho GLB, Rebouças Filho PP, de Medeiros FNS, Cortez PC (2014) Lung disease detection using feature extraction and extreme learning machine. Revista Brasileira de Engenharia Biomédica 30(3):207–214Google Scholar
  47. Ramalho GLB, Ferreira DS, Rebouças Filho PP, de Medeiros FNS (2016) Rotation-invariant feature extraction using a structural co-occurrence matrix. Measurement 94:406–415Google Scholar
  48. Reboucas ES, Braga AM, Marques RCP, Reboucas Filho PP (2016) A new approach to calculate the nodule density of ductile cast iron graphite using a level set. Measurement 89:316–321Google Scholar
  49. Rebouças Filho PP, Cortez PC, Holanda MA (2011) Active contour modes crisp: new technique for segmentation the lungs in CT images. Braz J Biomed Eng 27:259–272Google Scholar
  50. Rebouças Filho PP, Cortez PC, da Silva Barros AC, De Albuquerque VHC (2014) Novel adaptive balloon active contour method based on internal force for image segmentation—a systematic evaluation on synthetic and real images. Exp Syst Appl 41(17):7707–7721Google Scholar
  51. Rebouças Filho PP, Sarmento RM, Holanda GB, de Alencar LD (2017a) New approach to detect and classify stroke in skull CT images via analysis of brain tissue densities. Comput Methods Prog Biomed 148:27–43Google Scholar
  52. Rebouças Filho PP, Cortez PC, da Silva Barros AC, Albuquerque VHC, Tavares JMRS (2017b) Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images. Med Image Anal 35:503–516Google Scholar
  53. Rebouças Filho PP, Rebouças EDS, Marinho LB, Sarmento RM, Tavares JM, de Albuquerque VHC (2017c) Analysis of human tissue densities: a new approach to extract features from medical images. Pattern Recognit Lett 94:211–218Google Scholar
  54. Rebouças Filho PP, da Silva Barros AC, Ramalho GLB, Pereira CR, Papa JP, de Albuquerque VHC, Tavares JMRS (2017d) Automated recognition of lung diseases in ct images based on the optimum-path forest classifier. Neural Comput Appl 1–14Google Scholar
  55. Rebouças Filho PP, Peixoto SA, da Nóbrega RVM, Hemanth DJ, Medeiros AG, Sangaiah AK, de Albuquerque VHC (2018) Automatic histologically-closer classification of skin lesions. Comput Med Imaging Graph 68:40–54. Google Scholar
  56. Rekik I, Allassonnière S, Carpenter TK, Wardlaw JM (2012) Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal. NeuroImage Clin 1(1):164–178Google Scholar
  57. Rodrigues MB, Marinho LB, Nóbrega RVM, Souza JWM, Rebouças Filho PP (2016) Lung segmentation in chest computerized tomography images using the border following algorithm. In: International conference on intelligent systems design and applications. Springer, pp 539–548Google Scholar
  58. Rodrigues MB, Da Nóbrega RVM, Alves SSA, Rebouças Filho PP, Duarte JBF, Sangaiah AK, De Albuquerque VHC (2018) Health of things algorithms for malignancy level classification of lung nodules. IEEE Access 6:18592–18601Google Scholar
  59. Rouhi R, Jafari M, Kasaei S, Keshavarzian P (2015) Benign and malignant breast tumors classification based on region growing and CNN segmentation. Exp Syst Appl 42(3):990–1002Google Scholar
  60. Saad NM, Abdullah AR (2012) Automated region growing for segmentation of brain lesion in diffusion-weighted MRI. In: International multiconference of engineers and computer scientists (IMECS). Hong Kong, March 2012, pp 14–16Google Scholar
  61. Salman NH, Ghafour BM, Hadi GM (2015) Medical image segmentation based on edge detection techniques. Adv Image Video Process 3(2):1Google Scholar
  62. Sangaiah AK, Samuel OW, Li X, Abdel-Basset M, Wang H (2017) Towards an efficient risk assessment in software projects—fuzzy reinforcement paradigm. Comput Electr EngGoogle Scholar
  63. Sayah B, Tighiouart B (2014) Brain tumour segmentation in MRI: knowledge-based system and region growing approach. Int J Biomed Eng Technol 14(1):71–89Google Scholar
  64. Sethian JA (1999) Level set methods and fast merging methods: evolving interfaces in computational geometry, fluid mechanics, computer vision and materials science, 1st edn. Cambridge University Press, CambridgezbMATHGoogle Scholar
  65. Silva EM, Marinho LB, Leite JP, Leite JP, Fialho WML, de Albuquerque VHC, Tavares JMRS (2016) Classification of induced magnetic field signals for the microstructural characterization of sigma phase in duplex stainless steels. Metals 6(7):164Google Scholar
  66. Sun M, Hu R, Yu H, Zhao B, Ren H (2015) Intracranial hemorrhage detection by 3D voxel segmentation on brain CT images. In: International conference on wireless communications and signal processing (WCSP), Nanjing, China. IEEE, pp 1–5Google Scholar
  67. Tang F, Ng DKS, Chow DHK (2011) An image feature approach for computer-aided detection of ischemic stroke. Comput Biol Med 41(7):529–536Google Scholar
  68. Theodoridis S, Koutroumbas K (2009) Classifiers based on Bayes decision theory. In: Theodoridis S, Koutroumbas K (eds) Pattern recognition, 4th edn. Academic Press, Boston, pp 13–89Google Scholar
  69. Von Wangenheim A, Charnovscki R, Cardoso RCF, de Souza Nobre LF, Chaves D, Comunello E (2002) Cyclopsstrokequantifier-ischaemic stroke detection system using dynamic CT. In: Proceedings of the 15th IEEE symposium on computer-based medical systems, 2002. (CBMS 2002). IEEE, pp 251–256Google Scholar
  70. Wang X-Y, Bu J (2010) A fast and robust image segmentation using FCM with spatial information. Digit Signal Process 20(4):1173–1182Google Scholar
  71. Wang C, Frimmel H, Smedby Ö (2011) Level-set based vessel segmentation accelerated with periodic monotonic speed function. In: SPIE medical imaging, Lake Buena Vista, Florida, 2011. International Society for Optics and Photonics, pp 79621M–79621MGoogle Scholar
  72. Wang C, Frimmel H, Smedby Ö (2014) Fast level-set based image segmentation using coherent propagation. Med Phys 41(7):073501Google Scholar
  73. World Health Organization (2015) Cardiovascular diseases (cvds). Technical report, World Health Organization, WHOGoogle Scholar
  74. Yeung DY, Chow C (2002) Parzen-window network intrusion detectors. In: 16th International conference on pattern recognition, Quebec City, Canada, 2002. International Association of Pattern Recognition, pp 385–388Google Scholar
  75. Yousem DM, Grossman RI (2010) Neuroradiology: the requisites, 3rd edn. Elsevier Health Sciences, PhiladelphiaGoogle Scholar
  76. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128Google Scholar
  77. Zhang R, Shen J, Wei F, Li X, Sangaiah AK (2017) Medical image classification based on multi-scale non-negative sparse coding. Artif Intell Med 83:44–51Google Scholar
  78. Zucker SW (1976) Region growing: childhood and adolescence. Comput Graph Image Process 5(3):382–399Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto Federal de Federal de Educação, Ciência e Tecnologia do Ceará (IFCE)CearáBrazil
  2. 2.Programa de Pós-Graduação em Informática Aplicada, Laboratório de BioinformáticaUniversidade de FortalezaFortalezaBrazil

Personalised recommendations