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Microsystem Technologies
Erschienen in: Microsystem Technologies 2/2021

13.07.2018 | Technical Paper

Automatic MR image segmentation using maximization of mutual information

verfasst von: Apurba Roy, Santi P. Maity

Erschienen in: Microsystem Technologies | Ausgabe 2/2021

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Abstract

Magnetic resonance (MR) brain image segmentation is an important task for the early detection of any deformation followed by the quantitative analysis for the prediction and stage defection of brain diseases. But segmentation of the MR brain image suffers from limited accuracy as captured images have non-uniform homogeneity over an organ, presence of noise, uneven and broken boundary etc. Due to the complex structure of the brain and varieties of the captured MR images, only a single feature based MR image segmentation cannot give sufficient accurate result. In the proposed method thresholds for segmenting the MR image are computed by maximizing the mutual information for the two features, compactness and homogeneity. The proposed algorithm is tested against the real T1 MR image to asses the accuracy. Further the output is validated and compared with the ground truth and other recently reported works.
Vorheriger Artikel Preface of special issue of CCSN2016
Nächster Artikel Selective amino acid detection by green synthesized copper nanoparticles prepared using basil (Ocimum tenuiflorum) flower extract

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Literatur
Aubert-Broche B, Griffin M, Pike G, Evans A, Collins D (2006) Twenty new digital brain phantoms for creation of validation image data bases. IEEE Trans Med Imaging 25(11):1410–1415CrossRef
Bouhrara M, Bonny JM, Ashinsky BG, Maring MC, Spencer RG (2017) Noise estimation and reduction in magnetic resonance imaging using a new multispectral nonlocal maximum-likelihood filter. IEEE Trans Med Imaging 36(1):181–193CrossRef
Bourouis S, Hamrouni K (2010) 3d segmentation of mri brain using level set and unsupervised classification. Int J Image Gr World Scientif 10(1):135–154MathSciNetCrossRef
Cheng HD, Sun Y (2000) A hierarchical approach to color image segmentation using homogeneity. IEEE Trans Image Process 9(12):2071–2082CrossRef
Cheng J, Liu J, Xu Y, Yin F, Wong DWK, Tan NM, Tao D, Cheng CY, Aung T, Wong TY (2013) Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging 32(6):1019–1032CrossRef
de Moura Frana L, Amigo JM, Cairs C, Bautista M, Pimentel MF (2017) Evaluation and assessment of homogeneity in images. part 1: Unique homogeneity percentage for binary images. Chemom Intell Lab Syst 171:26–39CrossRef
Fattah SA, Lin CC, Kung SY (2011) A mutual information based approach for evaluating the quality of clustering. In: IEEE International conference on acoustics, speech and signal processing (ICASSP), Prague, pp 601–604
Garcia-Gomez VJ, A-Bayarri A, S-Requena R, Garca AM, M-Bonmat L, Naranjo V (2014) Design, development and implementation of semi-automated cine MR images segmentation pipeline using feature extraction and active contours. In: IEEE-EMBS International conference on biomedical and health informatics (BHI), Valencia, pp 161–164
Ismail M, Mostapha M, Soliman A, Nitzken M, Khalifa F, Elnakib A, Gimel G, Casanova M, El-Baz (2015) A Segmentation of infant brain MR images based on adaptive shape prior and higher-order MGRF. In: IEEE international conference on image processing (ICIP), Quebec City, pp 4327–4331
Jing F, Li M, Zhang HJ, Zhang B (2003) Unsupervised image segmentation using local homogeneity analysis. In: Circuits and systems, 2003. ISCAS ’03. Proceedings of the 2003 international symposium (ISCAS), Bangkok, vol 2, pp II–456–II–459
Kayalvizhi M, Kavitha G, Sujatha CM, Ramakrishnan S (2014) Analysis of alzheimer MR brain images using entropy based segmentation and minkowski functional. In: International conference on informatics, Electronics and Vision (ICIEV), Dhaka, pp 1–6
Lucchi A, Smith K, Achanta R, Knott G, Fua P (2012) Supervoxel-based segmentation of mitochondria in em image stacks with learned shape features. IEEE Trans Med Imaging 31(2):474–486CrossRef
Mathew J, Mekkayil L, Ramasangu H, Karthikeyan BR, Manjunath AG (2016) Robust algorithm for early detection of alzheimer’s disease using multiple feature extractions. In: IEEE Annual India Conference (INDICON), Bangalore, pp 1–6
Monteiro FC, Campilho AC (2006) Performance evaluation of image segmentation. In: Campilho A, Kamel MS (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg
Montero RS, Bribiesca E (2009) State of the art of compactness and circularity measures. Int Math Forum 4(27):1305–1335MathSciNetMATH
Ortiz A, Grriz JM, Ramirez J, Salas-Gonzalez D (2011) MR brain image segmentation by growing hierarchical som and probability clustering. Electron Lett 47(10):585–586CrossRef
Pal SK, Pal NR (1987) Segmentation using contrast and homogeneity measures. Pattern Recognit Lett 5(4):26–39CrossRef
Parameshwari DS, Aparna P (2014) An efficient algorithm for textural feature extraction and detection of tumors for a class of brain MR imaging applications. In: 19th International conference on digital signal processing (DSP), Hong Kong, pp 339–344
Qiu C, Xiao J, Yu L, Han L, Iqbal MN (2013) A modified interval type-2 fuzzy c-means algorithm with application in MR image segmentation. Pattern Recognit Lett 34(12):1329–1338CrossRef
Qiu W, Yuan J, Ukwatta E, Tessier D, Fenster A (2013) Three-dimensional prostate segmentation using level set with shape constraint based on rotational slices for 3d end-firing trus guided biopsy. J Med Phys 40:12–7 (072,903)
Roy A, Maity SP, Maity HK (2014) On maximization of fuzzy entropy for MR image segmentation at compressed sensing. In: First international conference on image processing, Applications and systems conference (IPAS), Sfax, vol. 35, pp 1–6
Siddique I, Bajwa IS, Naveed MS, Choudhary MA (2006) Automatic functional brain MR image segmentation using region growing and seed pixel. In: 2006 ITI 4th International conference on information communications technology, Cairo, pp 1–2
Song H, Bogdan IIM, Wang S, Dong W, Quan W, Dang W, Yu X (2016) Automatic schizophrenia discrimination on fNIRS by using PCA and SVM. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, pp 389–394
Tian Z, Liu L, Zhang Z, Fei B (2016) Superpixel-based segmentation for 3d prostate MR images. IEEE Trans Med Imaging 35(3):791–801CrossRef
Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson WE, Willsky A (2003) A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans Med Imaging 22(2):137–154CrossRef
Wang C, Goatman KA, MacGillivray T, Beveridge E, Koutraki Y, Boardman J et al (2015) Automatic multi-parametric MR registration method using mutual information based on adaptive asymmetric K-means binning. In: IEEE 12th international symposium on biomedical imaging (ISBI), New York, pp 1089–1092
Wang H, Xu H, Ahmed SN, Mandai M (2016) Computer aided detection of cavernous malformation in t2-weighted brain MR images. In: IEEE Healthcare innovation point-of-care technologies conference (HI-POCT), Cancun, pp 101–104
Wang L, Chen Y, Pan X, Hong X, Xia D (2010) Level set segmentation of brain magnetic resonance images based on local gaussian distribution fitting energy. J Neurosci Methods Elsevier 188(2):316–325CrossRef
Wangn H, Zhang H, Ray N (2013) Adaptive shape prior in graph cut image segmentation. Pattern Recognit 46:1409–1414CrossRef
Yang SC, He YJ, Wun YJ (2016) Designated target enhancement and segmentation in multi-spectral MR images. In: International symposium on computer, consumer and control (IS3C), pp 1059–1062
Yuan S, Ying W (2015) MR image segmentation algorithm based on non-local fuzzy C-means clustering. In: The 27th Chinese control and decision conference (CCDC), Qingdao, pp 1117–1122
Zhang Y, Li X, Gao X, Zhang C (2016) A simple algorithm of superpixel segmentation with boundary constraint. IEEE Trans Circuits Syst Video Technol 27(7):1502–1514CrossRef
Zhang Z, Zhang X, Zhang J (2009) Sar image processing based on fast discrete curvelet transform. Inf Technol Appl 3:28–31
Metadaten
Titel
Automatic MR image segmentation using maximization of mutual information
verfasst von
Apurba Roy
Santi P. Maity
Publikationsdatum
13.07.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Microsystem Technologies / Ausgabe 2/2021
Print ISSN: 0946-7076
Elektronische ISSN: 1432-1858
DOI
https://doi.org/10.1007/s00542-018-4031-y

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