A non-invasive and adaptive CAD system to detect brain tumor from T2-weighted MRIs using customized Otsu’s thresholding with prominent features and supervised learning

Gupta, Nidhi; Khanna, Pritee

doi:10.1016/j.image.2017.05.013

Cited by 43 publications

(14 citation statements)

References 23 publications

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“…A higher classification accurate feature is selected. Feature classification accuracy is 98% [14]. In this study, the nuclei based neural network classifies the tumor with the help of proposed features.…”

Section: Iirelated Workmentioning

confidence: 99%

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

et al. 2021

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Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element.. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel Labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it's accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art.

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Section: Iirelated Workmentioning

confidence: 99%

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

et al. 2021

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“…Detecting brain tumour in T2‐weighted brain MRI images was introduced by Gupta and Khanna [24], this method was examined using an adaptive and intrusive tumour analysis process. The method of Otsu's thresholding is done for segmentation, and MRI images are enhanced by the prepossessing technique.…”

Section: Related Workmentioning

confidence: 99%

“…Table 1 illustrates the comparison of classifier performance. The overall classification performance of proposed SVM–AEPO with the selected GLCM, and HWHT features is compared with different existing techniques local binary pattern‐Tamura‐SVM (LBP‐Tamura‐SVM) [24], Berkeley wavelet transformation‐SVM (BWT‐SVM) [37] and GLCM–SVM (GLCM) [22]. The accuracy of proposed SVM–AEPO is found 2% better than the GLCM–SVM.…”

Section: Experiments Analysismentioning

confidence: 99%

Performance enhancement of image segmentation analysis for multi‐grade tumour classification in MRI image

Kandan¹,

Murugeswari²

2020

IET image process

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Medical applications have a massive footprint in human's day‐to‐day life. Among that, MRI has a significant role, as it incorporates a significant impact on a brain tumour. Segmenting the tumour from MRI is substantial, but it is a time‐consuming process. Both the normal and abnormal tissues found in the brain look similar, which increases the difficulty of the tumour detection process. The digital image needs to be processed to obtain an exact tumour detection result. The tumour detection process comprises five different stages, such as pre‐processing, segmentation, feature extraction, feature selection, and classification. In this proposed work, hybrid wavelet Hadamard transform and grey‐level co‐occurrence matrix are included for feature extraction. Feature selection utilises sequential forward selection, which is an easy greedy search algorithm. This algorithm chooses only the predominant features for classification. The classification uses a hybrid support vector machine and adaptive emperor penguin optimisation. The experimental analysis shows the efficiency of the proposed work in terms of accuracy, specificity, and sensitivity values by computing the true positive, false positive, true negative, and false negative.

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“…The main objective of any CAD system is to have high accuracy rate and low processing time simultaneously. To attain these two performance parameters researchers club different existing traditional approaches like thresholding-based, 4 region-based, 5 and statistical-based 6 with emerging approaches such as machine learning-based, 7 and deep learning-based to achieve an optimum output. 8 The MR brain tumor modalities vary in tissue anatomy and structure based on the signals pulse parameters (repetition time and echo time).…”

Section: Introductionmentioning

confidence: 99%

An efficient Hadoop‐based brain tumor detection framework using big data analytic

Chahal

Pandey

2020

Softw Pract Exp

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The exponential increase of brain MR image data in the medical imaging field requires faster and accurate segmentation of tumor. The computer aided detection systems acting as a second option to experts, radiologists, and surgeons needs to be swift enough to handle parallelism. However, handling of massive MR data for segmentation with high accuracy and low processing time is significant concern of any framework. In this article, distributed platforms for brain tumor segmentation using hybrid weighted fuzzy approach integrated with Matlab Distributed Computing Server and Hadoop has been proposed. The approach is based on the fuzzification of the pixel values to achieve more meaningful clusters by grouping of large data into similar clusters. The article focuses on analyzing the performance of varying sized data sets using hybrid fuzzy clustering in MapReduce on Hadoop to deal with huge MR brain data cross clusters of commodity computers. For experimentation varying size of DICOM data set is processed through different number of clusters to compare the read, write, and processing time on each node. The read and write operation time elevates as the data size increasing is floated to multinode. However, the processing time of the proposed approach turns to be 35 min on single, whereas 3-node clusters process the same data set (215 MB) in 3.4 min. Furthermore, increasing the data set to 7.3 GB the 3-node cluster performs in 235.4 min which is greatly reduced from single node processing time of 2085.2 min.

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A non-invasive and adaptive CAD system to detect brain tumor from T2-weighted MRIs using customized Otsu’s thresholding with prominent features and supervised learning

Cited by 43 publications

References 23 publications

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

Performance enhancement of image segmentation analysis for multi‐grade tumour classification in MRI image

An efficient Hadoop‐based brain tumor detection framework using big data analytic

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