MRI Images Enhancement and Tumor Segmentation for Brain

Min, Aye; Kyu, Zin Mar

doi:10.1109/pdcat.2017.00051

Cited by 15 publications

(4 citation statements)

References 1 publication

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“…Next, the data is processed through WF to reduce noise and improve signal quality by trying to make the mean square error between the original image and the enhanced image as low as possible. 16 This study set the parameters as follows: set the kernel sizes (k) to 3×3, 5×5, 7×7, 9×9, and 11×11, respectively.…”

Section: Median and Wiener Filter (Mwf)mentioning

confidence: 99%

A comparative study of pre-processing methods to improve glioma segmentation performance in brain MRI using deep learning

Naknaem,

Kaewlek

2024

JAMS

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Background: Glioma is the most common brain tumor in adult patients and requires accurate treatment. The delineation of tumor boundaries must be accurate and precise, which is crucial for treatment planning. Currently, delineating boundaries for tumors is a tedious, time-consuming task and may be prone to human error among oncologists. Therefore, artificial intelligence plays a vital role in reducing these problems. Objective: This study aims to find a relationship between improving image enhancement and evaluating the performance of deep learning models for segmenting glioma image data on brain MRI images. Materials and methods: The BraTs2023 dataset was used in this study. The image dataset was converted from three dimensions to two dimensions and then subjected to pre-processing via four image enhancement techniques, including contrast-limited adaptive histogram equalization (CLAHE), gamma correction (GC), non-local mean filter (NLMF), and median and Wiener filter (MWF). Subsequently, it was evaluated for structural similarity index (SSIM) and mean squared error. The deep learning segmentation model was created using the U-Net architecture and assessed for dice similarity coefficient (DSC), accuracy, precision, recall, F1-score, and Jaccard index for tumor segmentation. Results: The performance of enhanced image results for CLAHE, GC, NLMF, and MWF techniques shows SSIM values of 0.912, 0.905, 0.999, and 0.911, respectively. The dice similarity coefficient (DSC) for segmentation without image enhancement was 0.817. The DSC of segmentation for CLAHE, GC, NLMF, and MWF techniques were 0.818, 0.812, 0.820, and 0.797, respectively. Conclusion: The enhanced image technique could affect the performance of tumor segmentation. by the enhanced image for use in a trained model may increase or decrease performance depending on the chosen image enhancement technique and the parameters determined by each method.

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Section: Median and Wiener Filter (Mwf)mentioning

confidence: 99%

A comparative study of pre-processing methods to improve glioma segmentation performance in brain MRI using deep learning

Naknaem,

Kaewlek

2024

JAMS

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“…Widyarto et al [9] proposed an image contrast enhancement method which applies the 2D-sigmoid function at tumour boundary. Min and Kyu [6] proposed an MRI images enhancement technique consists of median filtering, wiener filtering, adaptive k-means clustering, and morphological operation.…”

Section: Related Workmentioning

confidence: 99%

“…It is well known that in order to improve the recognition accuracy of brain tumours in the presence of artefacts in images, refined image preprocessing is necessary. After a lot of investigation on the glioma image processing domain, it is found that most MRI researchers focus on the segmentation, detection, and identification of lesions in brain tumour images [5][6][7]. In the aspect of glioma image enhancement, the research results are somewhat lacking.…”

Section: Introductionmentioning

confidence: 99%

An adaptive enhancement method based on stochastic parallel gradient descent of glioma image

Wang,

Peng,

et al. 2023

IET Image Processing

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Brain tumour diagnosis is significant for both physicians and patients, but the low contrast and the artefacts of MRI glioma images always affect the diagnostic accuracy. The existing mainstream image enhancement methods are insufficient in improving contrast and suppressing artefacts simultaneously. To enrich the field of glioma image enhancement, this research proposed a glioma image enhancement method based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. Firstly, this method modifies the cumulative distribution function on the image histogram and performs gamma correction on the image according to the modified histogram to obtain a contrast‐enhanced image. Then, the method suppresses the artefacts of glioma images by total variational and wavelet denoising algorithm. To get better enhancement images, the optimal parameters in the proposed method are searched by the SPGD algorithm. The statistical studies performed on 580 real glioma images demonstrate that the authors’ approach can outperform the existing mainstream image enhancement methods. The results show that the proposed method increases the discrete entropy of the image by 8.9% and the contrast by 2.8% compared to original images. The enhanced images are produced by the proposed method with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation.

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“…However, this approach cannot accommodate all the pixels of the MRI image. Another method that can be used is the unsupervised learning approach, such as clustering [13] and segmentation [14]. The latest segmentation technique used is the deep learning technique [15].…”

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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MRI Images Enhancement and Tumor Segmentation for Brain

Cited by 15 publications

References 1 publication

A comparative study of pre-processing methods to improve glioma segmentation performance in brain MRI using deep learning

A comparative study of pre-processing methods to improve glioma segmentation performance in brain MRI using deep learning

An adaptive enhancement method based on stochastic parallel gradient descent of glioma image

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

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