Top

Medical & Biological Engineering & Computing

Published in:

17-07-2021 | Original Article

B-Map: a fuzzy-based model to detect foreign objects in a brain

Authors: Dev Baloni, Shashi Kant Verma

Published in: Medical & Biological Engineering & Computing | Issue 7-8/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

To cope up with the medical complications, scientists and physicians rely more on digitized historical evidence. It helps them to identify the disease and to develop new drugs and strategies. The authors have designed a model called B-Map. It can detect and segmenting any foreign object in the brain using fuzzy rules. The model can detect objects such as cancer and brain tumor. The proposed work aims at designing a classifier. The classifier would help to detect all possible foreign objects using one application. B-Map has been compared with benchmark algorithms such as K-means and ANN. It was found that the proposed model performs significantly better than the current techniques. Original patients’ sample reports are taken from various medical laboratories.

Graphical abstract

The figure numbers are retained as in the paper. The proposed model is able to find the edges and segment different types of foreign objects or one can say unexpected developments. Figure 12 shows the outer edges of a section of a brain MRI. The patient’s MRI very clearly shows Hydrocephalus. The same is segmented and shown in Fig. 13. Figure 14 shows a segment of benign development and 15 shows a cancerous development which are again successfully segmented by the proposed model.

The data on which testing is done is clinical data of the original patients. As the patient's details and data cannot be shared the author's cannot upload the data in the repository. As soon as the research completes, a benchmark dataset will be created and uploaded in public domain so that researchers can access it.

previous article Detection of microsleep states from the EEG: a comparison of feature reduction methods

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Hachemi B, Oudjemia S, Alim F, Seddiki S, Talbi F, Abdelaziz M (2015) "Cerebralabnormalities detection by region-growing segmentation and KNNclassification," Fifth International Conference on theInnovative Computing Technology (INTECH 2015), pp 23–26. https://doi.org/10.1109/INTECH.2015.7173371

Chen S et al (2017) Low-Level Segmentation of 3-D magnetic resonance brain images-a rule-based system. Med Image Anal 35(3):807–810

El Hajj Chehade W et al (2014) Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI. Proc World Congr Intell Control Autom 57(Iccmc):1131–1136

Daniels CJ, Gallagher FA (2018) Unsupervised segmentation of 5D hyperpolarized carbon-13 MRI data using a Fuzzy Markov random field model. IEEE Trans Med Imaging 37(4):840–850CrossRef

Chen S, Ding C, Liu M (2019) Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recognit 88:90–100CrossRef

Singh G, Ansari MA (2017) Efficient detection of brain tumor from MRIs using K-means segmentation and normalized histogram. 20161st India International Conference on Information Processing (IICIP), pp 1–6. https://doi.org/10.1109/IICIP.2016.7975365

Hossain E, Hossain MF, Rahaman MA (2019) “An approach for the detection and classification of tumor cells from bone MRI using wavelet transform and KNN classifier”, 2018 Int. Conf Innov Eng Technol ICIET 2018:1–6

Ardon R, Cohen LD, Yezzi A (2007) ANew Implicit Method for Surface Segmentation by Minimal Paths:Applications in 3D Medical Images. In: Rangarajan A, Vemuri B, Yuille AL (eds) Energy Minimization Methods in Computer Vision andPattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science,vol 3757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11585978_34

Wang C, Li X, Wang W, Feng Y, Zhou Z, Zhan H (2011) Recognition of worm-eaten chestnuts based on machine vision. Math Comput Model 54(3–4):888–894CrossRef

10.

Batlle J, Casals A, Freixenet J (2000) A review on strategies for recognizing natural objects in colour images of outdoor scenes. 18:515–530. https://doi.org/10.1016/S0262-8856(99)00040-2

11.

Masood S, Sharif M, Masood A, Yasmin M, Raza M (2015) A survey on medical image segmentation. Curr Med Imaging Rev 11(1):3–14CrossRef

12.

Khan MW (2014) A survey : image segmentation techniques. Int J Image Graph 1:166–170. https://doi.org/10.12720/joig.1.4.166-170

13.

Ray PP (2017) A survey of IoT cloud platforms. Futur Comput Informatics J 1(1–2):35–46

14.

Steinhaus H (1956) Sur la division des corps materiels en parties. Bull Polish Acad Sci 4(3):801–804

15.

Singh M, Patel P, Khosla D, Kim T “Segmentation of functional MRI by K-means clustering,” in 1995 IEEE Nuclear Science Symposium and Medical Imaging Conference Record 3:1732–1736

16.

Sulaiman SN, Non NA, Isa IS, Hamzah N (2017) “Segmentation of brain MRI image based on clustering algorithm”, ISIEA 2014–2014 IEEE Symp. Ind Electron Appl 3(600):60–65

17.

El Hajj Chehade W, Kader RA, El-Zaart A (2018) “Segmentation of MRI images for brain cancer detection,” 2018 Int. Conf. Inf. Commun. Technol. ICOIACT 2018, vol. 2018-Janua, pp. 929–934. https://doi.org/10.1109/ICOIACT.2018.8350721

18.

Katz, Merickel (2003) “Translation-invariant aorta segmentation from magnetic resonance images,” International1989 Joint Conference on Neural Networks, 1989 1:327–333. https://doi.org/10.1109/IJCNN.1989.118604

19.

Joseph N, Sanghani P, Ren H (2017) “Brain MRI using machine learning techniques,” 16thIEEE International Conference on Machine Learning and Applications(ICMLA), 2017, pp 1149–1152. https://doi.org/10.1109/ICMLA.2017.00017

20.

Sezgin (2004) “Survey over image thresholding techniques and quantitative performance evaluation,” 13(1):146–165. https://doi.org/10.1117/1.1631315

21.

Lee HY, Codella NCF, Cham MD, Weinsaft JW, Wang Y (2010) Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI. IEEE Trans Biomed Eng 57(4):905–913CrossRef

22.

Zhao W, Wang L, Shi Y, Xi X, Yin Y, Tang Y (2017) “A multi-objective framework for brain MRI threshold segmentation,” Proc. - 2016 8th Int. Conf. Inf. Technol. Med. Educ. ITME 2016, pp. 20–24. https://doi.org/10.1109/ITME.2016.0015

23.

Salwe S, Raut R, Hajare P (2017) “Brain tumor pixels detection using adaptive wavelet based histogram thresholding and fine windowing,” 2016 Int. Conf. Inf. Technol. InCITe 2016 - Next Gener. IT Summit Theme - Internet Things Connect your Worlds, pp. 256–260. https://doi.org/10.1109/INCITE.2016.7857627

24.

Fenshia Singh J, Magudeeswaran V (2017) “Thresholding based method for segmentation of MRI brain images,” Proc. Int. Conf. IoT Soc. Mobile, Anal. Cloud, I-SMAC 2017, pp. 280–283. https://doi.org/10.1109/I-SMAC.2017.8058355

25.

Boyce JF (1991) “Segmentation of MR images using neural nets co-occurrence matrices” IEEColloquium on Image Processing in Medicine, pp. 5/1–5/4

26.

Amin SE, Mageed MA (2012) Brain tumor diagnosis systems based on artificial neural networks and segmentation using MRI. 20128th International Conference on Informatics and Systems (INFOS), pp. MM-119-MM-124

27.

Havaei M et al (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31CrossRef

28.

Li H, Li A, Wang M (2019) A novel end-to-end brain tumor segmentation method using improved fully convolutional networks. Comput Biol Med 108:150–160CrossRef

29.

Jodoin MHP, Larochelle H (2014) “Efficient interactive brain tumor segmentation as within-brain kNN classification,” pp. 556–561. https://doi.org/10.1109/ICPR.2014.106

30.

Peng Z, Wee W, Lee JH (2005) Mr brain imaging segmentation based on spatial Gaussian mixture model and Markov random field. Proc Int Conf Image Process ICIP 1(2):313–316

31.

Bricq S, Collet C, France F-I (2008) “3D Brain MRI segmentation based on robust hidden Markov chain Universit ´ e Strasbourg I LSIIT - UMR CNRS 7005 Pˆ ole API , Bd S . Brant , Universit ´ e Strasbourg I LINC - UMR CNRS 7191 4 rue Kirschleger F-67205 Strasbourg - France,” Magn. Reson. Imaging, pp. 517–520. https://doi.org/10.1109/ICASSP.2008.4517660

32.

Bauer S, Nolte L, Reyes M (2018)“Segmentation of brain tumor images based on Atlas-registration combined with a Markov-random-field lesion growth model ,” pp. 2018–2021

33.

Peis I et al (2017) “MRI brain segmentation using hidden Markov random fields with alpha-stable distributions,” 2016 IEEE Nucl. Sci. Symp. Med. Imaging Conf. Room-Temperature Semicond. Detect. Work. NSS/MIC/RTSD 2016, vol. 2017-Janua, no. 6

34.

Di Shi W, Wei Y (2012) “A brain segmentation algorithm based on Markov model fused with fuzzy similarity dynamic weights,” Proc. 2012 24th Chinese Control Decis. Conf. CCDC 2012, pp. 1461–1464. https://doi.org/10.1109/CCDC.2012.6244234

35.

Scrobotă I, G Băciuţ, Filip AG, Todor B, Blaga F, MF Băciuţ (2017) “Application of fuzzy logic in oral cancer risk assessment,” vol. 46, no. 5, pp. 612–619

36.

Bal A, Banerjee M, Chakrabarti A, Sharma P (2018) “MRI brain tumor segmentation and analysis using rough-Fuzzy C-means and shape based properties,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, pp. 1–19

37.

Yang Y (2008) “Modified fuzzy multi-thresholding algorithm for segmentation of MRI,” Proc World Congr Intell Control Autom 1131–1136

38.

Karnan M, Gopal NN (2010) “Hybrid Markov random field with parallel ant colony optimization and Fuzzy C means for MRI brain image segmentation,” 2010 IEEE Int. Conf. Comput. Intell. Comput. Res. ICCIC 2010, pp. 718–721

39.

Abdulbaqi HS, Jafri MZM, Omar AF, Bin Mustafa IS, Abood LK (2014) “Detecting brain tumor in magnetic resonance images using hidden Markov random fields and threshold techniques,” 2014 IEEE Student Conf. Res. Dev. SCOReD 2014, pp. 1–5. https://doi.org/10.1109/SCORED.2014.7072963

40.

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

41.

Nandi A (2016) “Detection of human brain tumour using MRI image segmentation and morphological operators,” 2015 IEEE Int. Conf. Comput. Graph. Vis. Inf. Secur. CGVIS 2015, pp. 55–60

42.

Liu J, Guo L (2015) “A new brain MRI image segmentation strategy based on K-means clustering and SVM,” Proc. - 2015 7th Int. Conf. Intell. Human-Machine Syst. Cybern. IHMSC 2015, vol. 2, pp. 270–273

43.

Pidchayathanakorn P, Supratid S (2015) “A hybrid of stationary wavelet thresholding and wiener filtering preprocess for noisy brain MRI spatial fuzzy segmentation,” ECTI-CON 2015 - 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), IEEE, Hua Hin, pp 1–6. https://doi.org/10.1109/ECTICon.2015.720709

44.

Irving B et al (2016) Pieces-of-parts for supervoxel segmentation with global context: application to DCE-MRI tumour delineation. Med Image Anal 32:69–83CrossRef

45.

Kumar PMS, Chatterjee S (2016) “Computer aided diagnostic for cancer detection using MRI images of brain

46.

Telrandhe SR, Pimpalkar A, Kendhe A (2016) “Detection of brain tumor from MRI images by using segmentation & SVM,” IEEE WCTFTR 2016 - Proc. 2016 World Conf. Futur. Trends Res. Innov. Soc. Welf

47.

Rao CH, Naganjaneyulu PV, Prasad KS (2017) “Brain tumor detection and segmentation using conditional random field,” Proc. - 7th IEEE Int. Adv. Comput. Conf. IACC 2017, pp. 807–810

48.

Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98–111CrossRef

49.

Srinivas B, Rao GS (2018) “Unsupervised learning algorithms for MRI brain tumor segmentation,” 2018 Conf. Signal Process. Commun. Eng. Syst. SPACES 2018, vol. 2018-Janua, pp 181–184

50.

Ajala Funmilola A, Oke OA, Adedeji TO, Alade OM, Adewusi EA (2012) “Fuzzy k-c-means clustering algorithm for medical image segmentation,” vol. 2, no. 6, pp 21–33

51.

MT Scholar, C. Science, C. Science, and C. Science (2015) “The K-means clustering based Fuzzy edge detection technique on MRI images

52.

Abdel-maksoud E, Elmogy M, Al-awadi R (2015) “Brain tumor segmentation based on a hybrid clustering technique,” Egypt Inform J 16(1):71–81. https://doi.org/10.1016/j.eij.2015.01.003

53.

Lang LF, Neumayer S, Öktem O, Schönlieb C (2019) “Template-based image reconstruction from sparse tomographic data,” Appl Math Optim 82:1081–1109. https://doi.org/10.1007/s00245-019-09573-2

Title: B-Map: a fuzzy-based model to detect foreign objects in a brain
Authors: Dev Baloni
Shashi Kant Verma
Publication date: 17-07-2021
Publisher: Springer Berlin Heidelberg
Published in: Medical & Biological Engineering & Computing / Issue 7-8/2021
Print ISSN: 0140-0118
Electronic ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-021-02367-1

Springer Professional

Abstract

Graphical abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 7-8/2021

Changes of biomechanics induced by Equistasi® in Parkinson’s disease: coupling between balance and lower limb joints kinematics

An efficient brain tumor image classifier by combining multi-pathway cascaded deep neural network and handcrafted features in MR images

Manufacturing and evaluation of a multi-purpose Iranian head and neck anthropomorphic phantom called MIHAN

Hierarchal Bayes model with AlexNet for characterization of M-FISH chromosome images

Non-obstructive monitoring of muscle fatigue for low intensity dynamic exercise with infrared thermography technique

Detection of microsleep states from the EEG: a comparison of feature reduction methods

Premium Partner