Modified FCM approach for MR brain image segmentation

Badmera, M. S.; Nilawar, Ajinkya P.; Karwankar, Anil

doi:10.1109/iccpct.2013.6528885

Cited by 7 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in medical images such as brain MRI that can be easily affected by unknown noises, the FCM performance is severely affected [ 52 ]. A number of researches have been performed to improve the limitation of FCM [ 53 , 54 , 55 , 56 ].…”

Section: Brain Tumor Segmentation Methodsmentioning

confidence: 99%

A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

View full text Add to dashboard Cite

A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.

show abstract

Section: Brain Tumor Segmentation Methodsmentioning

confidence: 99%

A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

View full text Add to dashboard Cite

show abstract

“…BRATS database provides access to the segmentation results that are manually performed in addition to the MR images of the brains with tumour. In the studies carried out, RI (Rand Index), VOI (Variation of Information) and GCE (Global Consistency Error) are among the methods that are frequently used for the performance comparison [14,15,16].…”

Section: Introductionmentioning

confidence: 99%

Brain MRI Segmentation based on Different Clustering Algorithms

Kucukkulahli¹,

Erdoğmuş²,

Polat³

2016

IJCA

View full text Add to dashboard Cite

In this study, MR Image segmentation has been realized with some clustering algorithms. In the study, the performances kmeans, lloyds, llyds-kmeans, pso clustering, ga clustering and jaya optimisation algorithms on some MR images from BRATS 2012 dataset have been compared. For the comparison, the manual segmentation results of MR images from BRATS 2012 dataset have been referenced and results have been compared with these referances. In the comparison RI (Rand Index), VOI (Variation of Information) and GCE (Global Consistency Error) have been used and results have been submitted. The results showed that the PSO algorithm yielded better results and has a better processing time than the other algorithms.

show abstract

Automatic Classification of Brain MRI Images Using SVM and Neural Network Classifiers

Natteshan

Jothi

2015

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Modified FCM approach for MR brain image segmentation

Cited by 7 publications

References 17 publications

A Survey of Brain Tumor Segmentation and Classification Algorithms

A Survey of Brain Tumor Segmentation and Classification Algorithms

Brain MRI Segmentation based on Different Clustering Algorithms

Automatic Classification of Brain MRI Images Using SVM and Neural Network Classifiers

Contact Info

Product

Resources

About