Brain tumor pixels detection using adaptive wavelet based histogram thresholding and fine windowing

Salwe, S R; Raut, Ranjana; Hajare, Pratik R.

doi:10.1109/incite.2016.7857627

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…Salwe et al 5 applied adaptive wavelet-based histogram thresholding technique on brain MRI image to detect and segment the abnormal regions from normal regions. The authors used fine windowing technique to segment the abnormal patterns in brain MRI image.…”

Section: R E L a T E D W O R K Smentioning

confidence: 99%

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

Arunachalam

Savarimuthu²

2017

Int J Imaging Syst Tech

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The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.

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Section: R E L a T E D W O R K Smentioning

confidence: 99%

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

Arunachalam

Savarimuthu²

2017

Int J Imaging Syst Tech

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“…Here DL has a clear advantage over the other methods. The segmentation through conventional image processing is the simple image processing operations on the pixel values of the MRI like multilevel thresholding [3], [4]. Various methods for segmenting the required portion from an MRI is developed over the years.…”

Section: Introductionmentioning

confidence: 99%

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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“…Resaeieh et al [22] have investigated microwave imaging for identification of brain tumor followed by process of performance verification. Salwe et al [23] have introduced an adaptive process of wavelet implementation where fine windowing operation as well as thresholds was used for detecting tumor cells of brain. Adoption of entropy factor toward similar detection problem was seen in the work carried out by Somwanshi et al [24] for enhancing the segmentation performance.…”

Section: Introductionmentioning

confidence: 99%

A novel framework for efficient identification of brain cancer region from brain MRI

Angadi¹,

Nagendra²,

Hanumanthappa³

2019

IJECE

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Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.

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Brain tumor pixels detection using adaptive wavelet based histogram thresholding and fine windowing

Cited by 9 publications

References 8 publications

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

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

A novel framework for efficient identification of brain cancer region from brain MRI

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