Application of Segmentation Methodology for Extracting MRI Brain Tumor Duly Mitigating the Noise

Umamaheswari, K.; Rajesh, P.; Rao, S. Srinivasa; Babu, P.

doi:10.1109/cicn.2015.64

Cited by 5 publications

(6 citation statements)

References 3 publications

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“…Where the superscript (1) indicates the first layer and ℎ (1) : ℝ (1) → ℝ (1) is the transfer function for the encoder, (1) ∈ ℝ (1) × is the weight matrix and (1) ∈ ℝ (1) is the bias vector.…”

Section: Image Reconstructionmentioning

confidence: 99%

“…Where the superscript (2) represents the second layer. ℎ (2) : ℝ → ℝ is the transfer function for the decoder, (2) ∈ ℝ × (1) is the weight matrix and (2) ∈ ℝ is the bias vector. The image reconstruction loss while minimizing the distance between the input vector and output vector during this process is as follows: =~‖ − ̂ ‖ 1 (5) So that the overall loss function is the sum of 2 encoders, 2 decoder loss along with auxiliary encoder, the low-rank loss, and the reconstruction loss.…”

Section: Image Reconstructionmentioning

confidence: 99%

“…The lower-dimensional data-optimized using SVD is fed to the final decoder stage for the proper reconstruction of the images. If the input to an autoencoder is ∈ ℝ , then the encoder maps the vector into a vector ∈ ℝ (1) as follows:…”

Section: Image Reconstructionmentioning

confidence: 99%

“…Automatic detection of tumor starts from simple thresholding [1], [2] and developed to various sophisticated methods like Deep Learning (DL). The methods are classified into three categories.…”

Section: Introductionmentioning

confidence: 99%

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A Dual Autoencoder and Singular Value Decomposition Based Feature Optimization for the Detection of Brain Tumor from MRI Images

Aswani

Menaka

2020

Preprint

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IntroductionThe brain tumor is the growth of abnormal cells inside the brain. These cells can be grown into malignant or benign tumors. Segmentation of tumor from MRI images using image processing techniques started decades back. Image processing based brain tumor segmentation can be divided in to three categories conventional image processing methods, Machine Learning methods and Deep Learning methods. Conventional methods lacks the accuracy in segmentation due to complex spatial variation of tumor. Machine Learning methods stand as a good alternative to conventional methods. Methods like SVM, KNN, Fuzzy and a combination of either of these provide good accuracy with reasonable processing speed. The difficulty in processing the various feature extraction methods and maintain accuracy as per the medical standards still exist as a limitation for machine learning methods. In Deep Learning features are extracted automatically in various stages of the network and maintain accuracy as per the medical standards. Huge database requirement and high computational time is still poses a problem for deep learning. MethodTo overcome the limitations specified above we propose an unsupervised dual autoencoder with latent space optimization here. The model require only normal MRI images for its training thus reducing the huge tumor database requirement. With a set of normal class data, an autoencoder can reproduce the feature vector into an output layer. This trained autoencoder works well with normal data while it fails to reproduce an anomaly to the output layer. But a classical autoencoder suffer due to poor latent space optimization. The Latent space loss of classical autoencoder is reduced using an auxiliary encoder along with the feature optimization based on Singular value Decomposition (SVD). The patches used for training are not traditional square patches but we took both horizontal and vertical patches to keep both local and global appearance features on the training set. An Autoencoder is applied separately for learning both horizontal and vertical patches. While training a logistic sigmoid transfer function is used for both encoder and decoder parts. SGD optimizer is used for optimization with an initial learning rate of .001 and the maximum epochs used are 4000. The network is trained in MATLAB 2018a with a processor capacity of 3.7 GHz with NVIDIA GPU and 16 GB of RAM.ResultsThe results are obtained using a patch size of 16x64, 64x16 for horizontal and vertical patches respectively. In Glioma images tumor is not grown from a point rather it spreads randomly. Region filling and connectivity operations are performed to get the final tumor segmentation. Overall the method segments Meningioma better than Gliomas. Three evaluation metrics are considered to measure the performance of the proposed system such as Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity.ConclusionAn unsupervised method for the segmentation of brain tumor from MRI images is proposed here. The proposed dual autoencoder with SVD based feature optimization reduce the latent space loss in the classical autoencoder. The proposed method have advantages in computational efficiency, no need of huge database requirement and better accuracy than machine learning methods. The method is compared Machine Learning methods Like SVM, KNN and supervised deep learning methods like CNN and commentable results are obtained.

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Section: Image Reconstructionmentioning

confidence: 99%

Section: Image Reconstructionmentioning

confidence: 99%

Section: Image Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Dual Autoencoder and Singular Value Decomposition Based Feature Optimization for the Detection of Brain Tumor from MRI Images

Aswani

Menaka

2020

Preprint

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“…The converse filtering is a reclamation system for deconvolution, i.e., when the picture is obscured by a known low pass channel, it is conceivable to recuperate the picture by opposite separating or summed up reverse separating [7] [5]. In any case, reverse sifting is extremely delicate to added substance commotion.…”

Section: Wiener Filteringmentioning

confidence: 99%

Image Denoising And Segmentation Approchto Detect Tumor From BRAINMRI Images

Rangaswamy¹,

B²,

Timmapur³

et al. 2018

Journal of Soft Computing and Applications

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The detection of the Brain Tumor is a challenging problem, due to the structure of the Tumor cells in the brain. This project presents a systematic method that enhances the detection of brain tumor cells and to analyze functional structures by training and classification of the samples in SVM and tumor cell segmentation of the sample using DWT algorithm. From the input MRI Images collected, first noise is removed from MRI images by applying wiener filtering technique. In image enhancement phase, all the color components of MRI Images will be converted into gray scale image and make the edges clear in the image to get better identification and improvised quality of the image. In the segmentation phase, DWT on MRI Image to segment the grey-scale image is performed. During the post-processing, classification of tumor is performed by using SVM classifier. Wiener Filter, DWT, SVM Segmentation strategies were used to find and group the tumor position in the MRI filtered picture respectively. An essential perception in this work is that multi arrange approach utilizes various leveled classification strategy which supports execution altogether. This technique diminishes the computational complexity quality in time and memory. This classification strategy works accurately on all images and have achieved the accuracy of 93%.

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Brain tumour detection using mean shift clustering and GLCM features with edge adaptive total variation denoising technique

Vallabhaneni

Rajesh

2018

Alexandria Engineering Journal

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Application of Segmentation Methodology for Extracting MRI Brain Tumor Duly Mitigating the Noise

Cited by 5 publications

References 3 publications

A Dual Autoencoder and Singular Value Decomposition Based Feature Optimization for the Detection of Brain Tumor from MRI Images

A Dual Autoencoder and Singular Value Decomposition Based Feature Optimization for the Detection of Brain Tumor from MRI Images

Image Denoising And Segmentation Approchto Detect Tumor From BRAINMRI Images

Brain tumour detection using mean shift clustering and GLCM features with edge adaptive total variation denoising technique

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