Brain tumor extraction from MRI image using mathematical morphological reconstruction

Sharma, Yamini; Meghrajani, Yogesh K.

doi:10.1109/et2ecn.2014.7044982

Cited by 17 publications

(9 citation statements)

References 4 publications

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“…The mentioned target can be artificial (for example, circular) or natural feature which is located within the defined effective area or region of interest (ROI). In the next stage, by filling the holes in the image and removing noises, the target is detected [ 47 ]. Finally, the 2D center position of each target is determined by the image analysis algorithm of region descriptor [ 48 ].…”

Section: Methodology Of Deformation Measurementmentioning

confidence: 99%

Developing an Optical Image-Based Method for Bridge Deformation Measurement Considering Camera Motion

Abolhasannejad

Huang

Namazi

2018

Sensors

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Since deformation estimation may lead to errors occurring when the camera vibrates, it is necessary to remove the image global motion before computing real bridge deformation. In this study, a combination of image motion correction algorithm and 2D image-based deformation measurement technique was utilized to address the issue of camera motion during the image data acquisition for bridge deformation measurement. Based on the proposed methodology, the image motion parameters were estimated by defining an effective sub-image in the image and using Iterative Affine Motion Estimator. Then the estimated parameters were applied to all pixels of each captured image to remove the motion effect. Finally, the corrected images were used to analyze by a 2D image-based deformation measurement technique in order to extract and measure real bridge deformation by tracking artificial or natural targets. The proposed methodology was validated by two experiments in the lab and field environments. Achieved results show the accuracy and reliability of the proposed methodology.

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Section: Methodology Of Deformation Measurementmentioning

confidence: 99%

Developing an Optical Image-Based Method for Bridge Deformation Measurement Considering Camera Motion

Abolhasannejad

Huang

Namazi

2018

Sensors

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“…They exploit underlying differences in intensity values between normal and abnormal regions. This encompasses watershed segmentation [28] followed by the application of some morphological operations to detect tumor regions in an MRI slice [8,29]. However image processing based approaches often suffer from severe over-segmentation and noise, in the form of false positive regions, resulting in inappropriately delineated tumor region.…”

Section: Overview Of Brain Tumor Detectionmentioning

confidence: 99%

A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks

Banerjee¹,

Sharma²,

Shankar³

2018

Preprint

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Inspired by the success of Convolutional Neural Networks (CNN), we develop a novel Computer Aided Detection (CADe) system using CNN for Glioblastoma Multiforme (GBM) detection and segmentation from multi channel MRI data. A two-stage approach first identifies the presence of GBM. This is followed by a GBM localization in each "abnormal" MR slice. As part of the CADe system, two CNN architectures viz. Classification CNN (C-CNN) and Detection CNN (D-CNN) are employed. The CADe system considers MRI data consisting of four sequences (T1, T1c, T2, and T2F LAIR) as input, and automatically generates the bounding boxes encompassing the tumor regions in each slice which is deemed abnormal. Experimental results demonstrate that the proposed CADe system, when used as a preliminary step before segmentation, can allow improved delineation of tumor region while reducing false positives arising in normal areas of the brain. The GrowCut method, employed for tumor segmentation, typically requires a foreground and background seed region for initialization. Here the algorithm is initialized with seeds automatically generated from the output of the proposed CADe system, thereby resulting in improved performance as compared to that using random seeds.

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“…Megersa and Alemu [22] have designed an automated framework based on neural network composed of preprocessing, image fusion, classification task for automatic image segmentation and tumor detection form brain MRI. In [23] In the same way Senthilkumaran and Thimmiaraja [26] have used Histogram equalization and Sharma and Meghrajani [27] have used mathematical morphological model to enhance the medical image visibility for efficiently analyzing barin tumor from MRI. The work of Sulaiman et al [28] presented a new model based on the clustering algorithm, which objective is to remove the salt and pepper niose from the brain MRI.…”

Section: Introductionmentioning

confidence: 99%

Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Processing Capability

Harish¹,

Ahammed²

2019

IJECE

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The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.

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Brain tumor extraction from MRI image using mathematical morphological reconstruction

Cited by 17 publications

References 4 publications

Developing an Optical Image-Based Method for Bridge Deformation Measurement Considering Camera Motion

Developing an Optical Image-Based Method for Bridge Deformation Measurement Considering Camera Motion

A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks

Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Processing Capability

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