An superior achievement of brain tumor detection using segmentation based on F-transform

Al-Azzawi, Nemir Ahmed; Sabir, Mohannad K.

doi:10.1109/wscnis.2015.7368302

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…The combined quantitative analysis is performed based on the pixel similarity of the resulting segmented image versus the manually segmented image. The precision is represented repeatability of segmentation, taking into account all subjective actions required to produce the product [30][31][32][33]. The possible results are expressed as familiar images correctly classified (FICC), familiar images abnormal classified as (FIAC), strange images correctly classified (SICC), and strange images abnormal classified (SIAC).…”

Section: Resultsmentioning

confidence: 99%

Microscopy images segmentation algorithm based on shearlet neural network

Al-Azzawi

2021

Bulletin EEI

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Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.

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Section: Resultsmentioning

confidence: 99%

Microscopy images segmentation algorithm based on shearlet neural network

Al-Azzawi

2021

Bulletin EEI

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“…This procedure does not evaluate any specific parameters. Al-Azzawi and Sabir [13] explained the Fuzzy transform segmentation which is used in brain tumor detection to handle the information and salient edges. There are two steps used in this method.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain tumor detection in magnetic resonance images

2017

IJLTET

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“…An automated extraction of potential tumors from brain scans is thus desirable and plausible. Much research and implementation have gone into tumor segmentation using the general image segmentation techniques which include Kmeans clustering [2], [3], Fuzzy C means clustering and watershed methods [4] [5] and artificial neural networks and machine learning techniques [6,7]. Other techniques like histogram based methods [8] and region based methods (region splitting, growing and merging) [9] have also been exploited.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation using SLIC Superpixels and Optimized Thresholding Algorithm

Adjei¹,

Nunoo‐Mensah²,

Agbesi³

et al. 2018

IJCA

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This paper deals with the implementation of a simple algorithm for automatic brain tumor segmentation. Brain tumor is commonly diagnosed by Computer tomography and Magnetic Resonance Imaging in clinical treatment. The paper uses Simple Linear Iterative Clustering (SLIC) to segment brain images according to their spatial and color proximities. The ratio of the mean and variance of the image pixels are determined in order to obtain an optimum threshold value. Region merging after thresholding was carried out. The final output image was an image with tumor sections circled out. The segmentation adheres to boundaries and the procedure is fast and reproducible.

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An superior achievement of brain tumor detection using segmentation based on F-transform

Cited by 5 publications

References 30 publications

Microscopy images segmentation algorithm based on shearlet neural network

Microscopy images segmentation algorithm based on shearlet neural network

Brain tumor detection in magnetic resonance images

Brain Tumor Segmentation using SLIC Superpixels and Optimized Thresholding Algorithm

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