Mid-sagittal plane detection in brain magnetic resonance image based on multifractal techniques

被引:12
作者
Ramasamy, Uthayakumar [1 ]
Arulprakash, Gowrisankar [1 ]
机构
[1] Gandhigram Rural Inst Deemed Univ, Dept Math, Dindigul 624302, Tamil Nadu, India
关键词
biomedical MRI; fractals; brain; biomedical measurement; medical image processing; midsagittal plane detection; brain magnetic resonance image; multifractal techniques; asymmetry; angular deviation; MSP; GENERALIZED DIMENSIONS; STRANGE ATTRACTORS; FRACTAL ANALYSIS; SYMMETRY PLANE; SEGMENTATION; TUMOR; ALGORITHMS; MRI;
D O I
10.1049/iet-ipr.2016.0003
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Human brain is separated into two hemispheres by the mid-sagittal plane (MSP) as bilateral symmetry. Extraction of this symmetry plane from magnetic resonance images is one of the precise processes for diagnosis. The foremost challenge of this work is to analyse the degree of asymmetry between hemispheres. Most of the existing work has analysed primarily on the image intensity to estimate the asymmetry between hemispheres. The present study explores the possibility of the generalised fractal dimensions to measure the asymmetry between hemispheres, in addition multifractal spectra applies to refine the optimal region of interest which characterises the complexity and homogeneity of an object. In order to validate the efficiency of the proposed technique, experimental results are compared with three state-of-the-art methods by the performance evaluation metrics such as yaw angle error and roll angle error. Besides, angular deviation and average deviation of distance between ground truth line and extracted MSP by the developed method is compared.
引用
收藏
页码:751 / 762
页数:12
相关论文
共 50 条
[31]   A Baboon Brain Atlas for Magnetic Resonance Imaging and Positron Emission Tomography Image Analysis [J].
Agaronyan, Artur ;
Syed, Raeyan ;
Kim, Ryan ;
Hsu, Chao-Hsiung ;
Love, Scott A. ;
Hooker, Jacob M. ;
Reid, Alicia E. ;
Wang, Paul C. ;
Ishibashi, Nobuyuki ;
Kang, Yeona ;
Tu, Tsang-Wei .
FRONTIERS IN NEUROANATOMY, 2022, 15
[32]   A Magnetic Resonance Image Based Atlas of the Rabbit Brain for Automatic Parcellation [J].
Munoz-Moreno, Emma ;
Arbat-Plana, Ariadna ;
Batalle, Dafnis ;
Soria, Guadalupe ;
Illa, Miriam ;
Prats-Galino, Alberto ;
Eixarch, Elisenda ;
Gratacos, Eduard .
PLOS ONE, 2013, 8 (07)
[33]   Automated unsupervised learning-based clustering approach for effective anomaly detection in brain magnetic resonance imaging (MRI) [J].
Govindaraj, Vishnuvarthanan ;
Thiyagarajan, Arunprasath ;
Rajasekaran, Pallikonda ;
Zhang, Yudong ;
Krishnasamy, Rajesh .
IET IMAGE PROCESSING, 2020, 14 (14) :3516-3526
[34]   A fuzzy logic-based meningioma tumor detection in magnetic resonance brain images usingCANFISandU-Net CNNclassification [J].
Ragupathy, Balakumaresan ;
Karunakaran, Manivannan .
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2021, 31 (01) :379-390
[35]   Novel fuzzy clustering-based bias field correction technique for brain magnetic resonance images [J].
Mishro, Pranaba K. ;
Agrawal, Sanjay ;
Panda, Rutuparna ;
Abraham, Ajith .
IET IMAGE PROCESSING, 2020, 14 (09) :1929-1936
[36]   Generalised rough intuitionistic fuzzy c-means for magnetic resonance brain image segmentation [J].
Namburu, Anupama ;
Samayamantula, Srinivas Kumar ;
Edara, Srinivasa Reddy .
IET IMAGE PROCESSING, 2017, 11 (09) :777-785
[37]   Direct Sagittal Image Registration and Tumor Delineation on Sagittal Magnetic Resonance Imaging Sequences for Image-Guided Brachytherapy of Cervical Cancer [J].
Radawski, Jeffrey D. ;
Huang, Zhibin ;
Wang, Jian Z. ;
Yuh, William T. C. ;
Mayr, Nina A. .
DISCOVERY MEDICINE, 2012, 13 (68) :47-56
[38]   Review of Set Theoretic Approaches to Magnetic Resonance Brain Image Segmentation [J].
Namburu, Anupama ;
Kumar, Samayamantula Srinivas ;
Reddy, Edara Srinivasa .
IETE JOURNAL OF RESEARCH, 2019, 68 (01) :350-367
[39]   LEGION - Based segmentation of magnetic resonance images of the brain [J].
Sivaradje, G ;
Saraswady, D ;
Dananjayan, P .
IETE JOURNAL OF RESEARCH, 2002, 48 (3-4) :311-315
[40]   Pixel-based Bayesian Classification for Meningioma Brain Tumor Detection using Post Contrast T1-weighted Magnetic Resonance Image [J].
Koley, Subhranil ;
Das, Dev Kumar ;
Chakraborty, Chandan ;
Sadhu, Anup K. .
2014 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT), 2014, :358-363