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

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Abstract

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.

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Adapted from Reboucas et al. (2016)

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Adapted from Sethian (1999)

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Notes

  1. http://www.mia-solution.com.

  2. each pixel z in the image is defined as a vector of dimension d.

References

  • 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):197

    Article  Google Scholar 

  • Aicha B, Abdelhafid B (2012) Morphological segmentation of the spleen from abdominal CT images. Int J Image Graph Signal Process 4(4):56

    Article  Google Scholar 

  • 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 49–60

  • 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):368–374

    Article  Google Scholar 

  • Beucher S, Lantuéjoul C (1979) Use of watersheds in contour detection. In: International conference on image processing, Rennes, França, 1979. Societá Astronómica Italiana, pp 1–10

  • Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, Norwell

    Book  MATH  Google Scholar 

  • Bhadauria HS, Singh A, Dewal ML (2013) An integrated method for hemorrhage segmentation from brain CT imaging. Comput Electr Eng 39(5):1527–1536

    Article  Google Scholar 

  • 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 1955–1959

  • Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, Secaucus

    MATH  Google Scholar 

  • Caselles V, Sapiro G, Chung DH (2000) Vector median filters, vector morphology, and coupled PDE’s: Theoretical connections. J Math Imaging Vis 12:109–120

    Article  MATH  Google Scholar 

  • 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):453–461

    Article  Google Scholar 

  • 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):9–15

    Article  Google Scholar 

  • 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 957–964

  • 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 773–780

  • 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):50–59

    Article  MathSciNet  Google Scholar 

  • 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 2017:1–2

    Article  Google Scholar 

  • 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 2018:1–8

    Article  Google Scholar 

  • Gillebert CR, Humphreys GW, Mantini D (2014) Automated delineation of stroke lesions using brain CT images. NeuroImage Clin 4:540–548

    Article  Google Scholar 

  • 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):573–584

    Article  Google Scholar 

  • Gonzalez RC, Woods RE (2010) Processamento Digital de Imagens, 3rd edn. Pearson Prentice Hall, São Paulo

    Google Scholar 

  • 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):447–458

    Article  Google Scholar 

  • 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 120:150–168

    Article  Google Scholar 

  • Jaccard P (1901) Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Impr, Corbaz

  • Kavitha AR, Chellamuthu C (2013) Detection of brain tumour from MRI image using modified region growing and neural network. Imaging Sci J 61(7):556–567

    Article  Google Scholar 

  • 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):189–224

    Article  Google Scholar 

  • 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–61446Q

  • 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):1940–1949

    Article  MathSciNet  MATH  Google Scholar 

  • 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):2095–2128

    Article  Google Scholar 

  • 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 240:89–100

    Article  Google Scholar 

  • 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 72:1–17

    Article  Google Scholar 

  • 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 68:26–43

    Article  Google Scholar 

  • 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 219–223

  • Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta (BBA) Protein Struct 405(2):442–451

    Article  Google Scholar 

  • 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 14–15

    Google Scholar 

  • 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):390–406

    Article  Google Scholar 

  • Mitiche A, Ayed IB (2010) Variational and level set methods in image segmentation. Springer, Berlin

    MATH  Google Scholar 

  • 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 45:294–306

    Article  Google Scholar 

  • Naidich TP, Castillo M, Cha S, Smirniotopoulos JG (2012) Imaging of the brain: expert radiology series, 1st edn. Elsevier Health Sciences, Philadelphia

    Google Scholar 

  • 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 337–340

  • 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):272–276

    Article  Google Scholar 

  • Neto EC, Gomes SL, Rebouças Filho PP, de Albuquerque VHC (2015c) Brazilian vehicle identification using a new embedded plate recognition system. Measurement 70:36–46

    Article  Google Scholar 

  • 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):254–258

    Article  Google Scholar 

  • 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 2495–2498

  • 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):524–533

    Article  Google Scholar 

  • Rajini NH, Bhavani R (2013) Computer aided detection of ischemic stroke using segmentation and texture features. Measurement 46(6):1865–1874

    Article  Google Scholar 

  • 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):207–214

    Article  Google Scholar 

  • Ramalho GLB, Ferreira DS, Rebouças Filho PP, de Medeiros FNS (2016) Rotation-invariant feature extraction using a structural co-occurrence matrix. Measurement 94:406–415

    Article  Google Scholar 

  • 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 89:316–321

    Article  Google Scholar 

  • 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 27:259–272

    Google Scholar 

  • 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):7707–7721

    Article  Google Scholar 

  • 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 148:27–43

    Article  Google Scholar 

  • 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 35:503–516

    Article  Google Scholar 

  • 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 94:211–218

    Article  Google Scholar 

  • 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 1–14

  • 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. https://doi.org/10.1016/j.compmedimag.2018.05

    Article  Google Scholar 

  • 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):164–178

    Article  Google Scholar 

  • 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 539–548

  • 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 6:18592–18601

    Article  Google Scholar 

  • 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):990–1002

    Article  Google Scholar 

  • 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 14–16

  • Salman NH, Ghafour BM, Hadi GM (2015) Medical image segmentation based on edge detection techniques. Adv Image Video Process 3(2):1

    Article  Google Scholar 

  • 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 Eng

  • Sayah B, Tighiouart B (2014) Brain tumour segmentation in MRI: knowledge-based system and region growing approach. Int J Biomed Eng Technol 14(1):71–89

    Article  Google Scholar 

  • 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, Cambridge

    MATH  Google Scholar 

  • 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):164

    Article  Google Scholar 

  • 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 1–5

  • Tang F, Ng DKS, Chow DHK (2011) An image feature approach for computer-aided detection of ischemic stroke. Comput Biol Med 41(7):529–536

    Article  Google Scholar 

  • 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 13–89

    Chapter  MATH  Google Scholar 

  • 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 251–256

  • Wang X-Y, Bu J (2010) A fast and robust image segmentation using FCM with spatial information. Digit Signal Process 20(4):1173–1182

    Article  Google Scholar 

  • 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–79621M

  • Wang C, Frimmel H, Smedby Ö (2014) Fast level-set based image segmentation using coherent propagation. Med Phys 41(7):073501

    Article  Google Scholar 

  • World Health Organization (2015) Cardiovascular diseases (cvds). Technical report, World Health Organization, WHO

  • 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 385–388

  • Yousem DM, Grossman RI (2010) Neuroradiology: the requisites, 3rd edn. Elsevier Health Sciences, Philadelphia

    Google Scholar 

  • 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):1116–1128

    Article  Google Scholar 

  • 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 83:44–51

    Article  Google Scholar 

  • Zucker SW (1976) Region growing: childhood and adolescence. Comput Graph Image Process 5(3):382–399

    Article  MathSciNet  Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  1. Instituto Federal de Federal de Educação, Ciência e Tecnologia do Ceará (IFCE), Ceará, Brazil

    Elizângela de S. Rebouças, Regis C. P. Marques, Alan M. Braga, Saulo A. F. Oliveira & Pedro P. Rebouças Filho

  2. Programa de Pós-Graduação em Informática Aplicada, Laboratório de Bioinformática, Universidade de Fortaleza, Fortaleza, CE, Brazil

    Victor Hugo C. de Albuquerque

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  1. Elizângela de S. Rebouças
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Communicated by A. K. Sangaiah, H. Pham, M.-Y. Chen, H. Lu, F. Mercaldo.

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Rebouças, E.d.S., Marques, R.C.P., Braga, A.M. et al. New level set approach based on Parzen estimation for stroke segmentation in skull CT images. Soft Comput 23, 9265–9286 (2019). https://doi.org/10.1007/s00500-018-3491-4

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