Automated brain tumor segmentation from mri data based on exploration of histogram characteristics of the cancerous hemisphere

Akter, Mir Khadiza; Khan, Shakil Mahmud; Azad, Samee; Fattah, Shaikh Anowarul

doi:10.1109/r10-htc.2017.8289080

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…When the proposed method is compared with similar approaches in the literature [11], [12], [13], [14], [15], it is seen that reasonable results can be obtained with a less complex algorithm. It can be said that the proposed method is less complex in terms of computational complexity, memory complexity and explainability.…”

Section: B Segmentation Resultsmentioning

confidence: 76%

“…After the SVM classification process, k-means clustering method has been used for segmentation, followed by morphological operations and acquired high segmentation accuracy on real life data. Akter et al [14] have developed an uncommon approach which divides the input image into hemispheres and selecting the one which includes the tumorous tissue. Intensity histograms have been used to detect the tumorous hemisphere.…”

Section: B Brain Tumor Segmentationmentioning

confidence: 99%

“…The automatic segmentation of glioma tumors covered in this study can assist radiologists and oncologists in their diagnosis and treatment processes. In the literature, various studies using histogram features, thresholding and morphological operations have shown reasonable success, offering an alternative, simpler and efficient way compared to DL based approaches [11], [12], [13], [14], [15]. Thus, a novel adaptive thresholding approach is developed in this study, taking the advantage of feature fusion and morphological operations inspired by the literature.…”

Section: Segmentationmentioning

confidence: 99%

“…One of the most effective solutions is to train the models on a wide variety of data by creating a new dataset for each MR device, which overcomplicates the problem. Histogram features, thresholding and morphological operations have been widely used as an alternative for DL approaches like in [11], [12], [13], [14], and [15] and acquired comparable success. Hence, a novel approach for brain tumor segmentation based on adaptive thresholding has been proposed in this study.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Glioma Grade Classification using CNNs and Segmentation with an Adaptive Approach using Histogram Features in Brain MRIs

Özkaya

Sağıroğlu

2023

IEEE Access

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Artificial intelligence (AI) applications have become popular due to their advantages in solving health problems with high accuracy and confidence. One such application is the diagnosis of brain tumors or anomalies. This paper presents two new approaches for brain tumor grade classification and segmentation. Convolutional neural network (CNN) models were used as the first approach to classify High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) tumors and achieved with 99.85% accuracy, 99.85% F1 and 99.92% AUC scores. A new pipeline consisting of normalization, modality fusion and CNN model for HGG-LGG classification tasks was also proposed and developed. A novel algorithm based on histograms, thresholding and morphological filtering with feature fusion was also proposed and developed for the segmentation task. 70.58% Dice Similarity (DS) on average was achieved with the complete tumor segmentation. Experimental results have shown that the proposed algorithm has improved to measure the complete tumor region 15% more compared to the fixed thresholding. Segmentation results also encourage that the algorithm can be used as a feature extraction process on different sized brain MR images. It is expected that the extracted center of gravity features can be further used in AI algorithms for better segmentation, including T1 and T1CE modalities.

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Section: B Segmentation Resultsmentioning

confidence: 76%

Section: B Brain Tumor Segmentationmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Glioma Grade Classification using CNNs and Segmentation with an Adaptive Approach using Histogram Features in Brain MRIs

Özkaya

Sağıroğlu

2023

IEEE Access

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“…The complexity of the MRI image as the structure and morphology of the human brain is very complex, brain MRI images tend to have low contrast [9], [10], a relatively large manual segmentation time. It requires very high accuracy so that no pixel is missed considering that each pixel contains important information, and in some cases, different MRI images show distinct complexity even though they represent the same tumor type.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation using double density dual tree complex wavelet transform combined with convolutional neural network and genetic algorithm

Samosir¹,

Abdurachman

Gaol

et al. 2022

IJ-AI

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<span>Image segmentation is often faced by low contrast, bad boundaries, and inhomogeneity that made it difficult to separate normal and abnormal tissue. Therefore, it takes long periodto read and diagnose brain tumor patients. The aim of this study was to applied hybrid methods to optimize segmentation process of magnetic resonance image of brain. In this study, we divide the brain tumor images with double density dual-tree complex wavelet transform (DDDTCWT), continued by convolutional neural network (CNN), and optimized by genetic algorithm (GA) with 48 combinations yielding excellent results. The F-1 score was 99.42%, with 913 images test data. The training images consist of 1397 normal MRI images and 302 tumor magnetic resonance imaging (MRI) images resized by 32 x32 pixels. The DDDTCWT transforms the input images into more detail than ordinary wavelet transforms, and the CNNs will recognize the pattern of the output images. Additionally, we applied the GA to optimize the weights and biases from the first layer of the CNNs layers. The parameters used for evaluating were dice similarity coefficient (DSC), positive present value (PPV), sensitivity, and accuracy. The result showed that the combination of DDDTCWT, CNN, and GA could be used to brain MRI images and it generated parameters value more that 95%.</span>

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Review on Brain Tumor Segmentation: Hard and Soft Computing Approaches

Mishra¹,

Garg²,

Gupta³

et al. 2020

Advances in Intelligent Systems and Computing

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Automated brain tumor segmentation from mri data based on exploration of histogram characteristics of the cancerous hemisphere

Cited by 9 publications

References 2 publications

Glioma Grade Classification using CNNs and Segmentation with an Adaptive Approach using Histogram Features in Brain MRIs

Glioma Grade Classification using CNNs and Segmentation with an Adaptive Approach using Histogram Features in Brain MRIs

Brain tumor segmentation using double density dual tree complex wavelet transform combined with convolutional neural network and genetic algorithm

Review on Brain Tumor Segmentation: Hard and Soft Computing Approaches

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