Variable Variance Adaptive Mean-Shift and possibilistic fuzzy C-means based recursive framework for brain MR image segmentation

Aparajeeta, Jeetashree; Mahakud, Sasmita; Nanda, Pradipta Kumar; Das, Niva

doi:10.1016/j.eswa.2017.09.049

Cited by 11 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BRATSdataset (Namburu,kumarSamay,&Edara,2017)(Cabria&Gondra,2017 (Angulakshmi &Priya,BraintumoursegmentationfromMRIusingsuperpixelsbasedspectralclustering,2018) (Raju,Suresh,&Rao,2018) 2. Brain Web (Namburu, kumarSamay, & Edara, 2017) (Aparajeeta, Mahakud, Nanda, & Das, 2018)(Ji,Sun,Xia,Chen,Xia,&Feng,2012)(Ji,Sun,Xia,Chen,Xia,&Feng,2012) 3. IBSR (Namburu,kumarSamay,&Edara,2017) (Ghosh,Mali,&Das,2018) 4.…”

Section: Datasetsmentioning

confidence: 99%

A Systematic Review of Clustering and Classifier Techniques for Brain Tumor Segmentation in MRI Images

Mulimani¹,

Sannakki²,

Rajpurohit³

2021

International Journal of Computer Vision and Image Processing

View full text Add to dashboard Cite

MRI technique is widely used in the field of medicine because of its high spatial resolution, non-invasive characteristics, and soft tissue contrast. In this review article, a systematic study has been conducted to analyze the performance and issues of various techniques for brain tumor segmentation. Latest research on BTS in MRI with the higher resolution is utilized for the systematic review. The high-resolution images increase execution time of the classification, and accuracy is the other problem in BTS. Still, there is some research lacking in accuracy on the brain segmentation. Few researchers carried out the classification of different kinds of tissues in the brain images and also on the prediction on growth of tumor. Each method has specific technique to improve the performance of the BTS, and these methods are compared with one another in terms of result. Research comparison helps to understand the proposed method with their achieved results. Clustering algorithms such as K-means and FCM are generally used for segmentation, and GA, ANN, ANFIS, FCNN, SVM are commonly used as classifiers.

show abstract

Section: Datasetsmentioning

confidence: 99%

A Systematic Review of Clustering and Classifier Techniques for Brain Tumor Segmentation in MRI Images

Mulimani¹,

Sannakki²,

Rajpurohit³

2021

International Journal of Computer Vision and Image Processing

View full text Add to dashboard Cite

show abstract

“…The extension paper related to the mean-shift algorithm that proof the convergence step in the mean-shift algorithm is proposed by Ghassabeh (2013). In the medical field, the mean-shift algorithm has been applied to medical research (Vallabhaneni and Rajesh, 2017), (Aparajeeta et al, 2018), (Guo et al, 2018;Mure et al, 2015;Bai et al, 2013;Yang et al, 2013). The idea of mean-shift algorithm is to find pixel with similar characteristics of the density and save the distinct pixel value.…”

Section: Mean-shift Algorithmmentioning

confidence: 99%

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Haryanto¹,

Pratama²,

Suhartanto³

et al. 2020

Journal of Computer Science

View full text Add to dashboard Cite

Cancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data. This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a meanshift filter as the data pre-processing of the images. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72±0.48 for four cross-validations. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.

show abstract

“…For example, both random forests [2][3][4] and support vector machines [5] are typical supervised techniques commonly used in MRI segmentation. Recently, some new unsupervised algorithms have been proposed for extracting the required brain tissue, including grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) [6,7], but clustering analysis is among the most studied [8,9]. It has been found that clustering performance depends heavily on the selection of the initial cluster centres, and the segmentation results are sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Brain MR Images by Using Fully Convolutional Network and Gaussian Mixture Model with Spatial Constraints

Lai

Zhu

Ling

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Accurate segmentation of brain tissue from magnetic resonance images (MRIs) is a critical task for diagnosis, treatment, and clinical research. In this paper, a novel algorithm (GMMD-U) that incorporates the modified full convolutional neural network U-net and Gaussian-Dirichlet mixture model (GMMD) with spatial constraints is presented. The proposed GMMD-U considers the local spatial relationships by assuming that the prior probability obeys the Dirichlet distribution. Specifically, GMMD is applied for extracting brain tissue that has a distinct intensity region and modified U-net is exploited to correct the wrong-classification areas caused by GMMD or other conventional approaches. The proposed GMMD-U is designed to take advantage of the statistical model-based segmentation techniques and deep neural network. We evaluate the performance of GMMD-U on a publicly available brain MRI dataset by comparing it with several existing algorithms, and the results reported reveal that the proposed framework can accurately detect the brain tissue from MRIs. The proposed learning-based integrated framework could be effective for brain tissue segmentation, which will be helpful for surgeons in brain disease diagnosis.

show abstract

Variable Variance Adaptive Mean-Shift and possibilistic fuzzy C-means based recursive framework for brain MR image segmentation

Cited by 11 publications

References 34 publications

A Systematic Review of Clustering and Classifier Techniques for Brain Tumor Segmentation in MRI Images

A Systematic Review of Clustering and Classifier Techniques for Brain Tumor Segmentation in MRI Images

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Segmentation of Brain MR Images by Using Fully Convolutional Network and Gaussian Mixture Model with Spatial Constraints

Contact Info

Product

Resources

About