• Corpus ID: 8008091

Automated Tumor Segmentation and Brain Mapping for the Tumor Area

  title={Automated Tumor Segmentation and Brain Mapping for the Tumor Area},
  author={Pranay Manocha and Snehal Bhasme and Tanvi Gupta and Bijaya Ketan Panigrahi and Tapan Kumar Gandhi},
Magnetic Resonance Imaging (MRI) is an important diagnostic tool for precise detection of various pathologies. Magnetic Resonance (MR) is more preferred than Computed Tomography (CT) due to the high resolution in MR images which help in better detection of neurological conditions. Graphical user interface (GUI) aided disease detection has become increasingly useful due to the increasing workload of doctors. In this proposed work, a novel two steps GUI technique for brain tumor segmentation as… 
1 Citations

Figures from this paper

Machine learning for bioinformatics and neuroimaging

It is shown how ML techniques such as clustering, classification, embedding techniques and network‐based approaches can be successfully employed to tackle various problems such as gene expression clusters, patient classification, brain networks analysis, and identification of biomarkers.



A novel methodology for brain tumor detection based on two stage segmentation of MRI images

A novel methodology of two stage segmentation using Gabor filter banks generated using different frequencies and orientations for the detection and analysis of the brain tumor.

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

Enhanced Convolutional Neural Networks (ECNN) is proposed with loss function optimization by BAT algorithm for automatic segmentation method and the experimental result shows the better performance while comparing with the existing methods.

Model-based brain and tumor segmentation

This work presents an extension to an existing expectation maximization (EM) segmentation algorithm that modifies a probabilistic brain atlas with an individual subject's information about tumor location obtained from subtraction of post- and pre-contrast MRI.

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

This paper proposes an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels, which allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network.

Brain Tumor Segmentation in Multi-modality MRIs Using Multiple Classifier System and Spatial Constraint

This paper uses the intensities of different modalities in MRIs to represent the features of both normal and abnormal tissues, and the multiple classifier system (MCS) is applied to calculate the probabilities of brain tumor and normal brain tissue in the whole image.

3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set

A variational brain tumor segmentation algorithm is proposed that extends current approaches from texture segmentation by using a high dimensional feature set calculated from MRI data and registered atlases and shows that using a conditional model to discriminate between normal and abnormal regions significantly improves the segmentation results compared to traditional generative models.

Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification

A Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free, and integrated into the multilevel segmentation by weighted aggregation algorithm for glioblastoma multiforme brain tumor.

Automated segmentation of MR images of brain tumors.

The automated method allowed rapid identification of brain and tumor tissue with an accuracy and reproducibility comparable to those of manual segmentation, making automated segmentation practical for low-grade gliomas and meningiomas.

Automated model-based tissue classification of MR images of the brain

The algorithm is able to segment single- and multi-spectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's).