Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation

Noor, Noorhayati Mohamed; Khalid, Norlin; Hassan, Rohaida; Ibrahim, Shafaf; Yassin, Ihsan Mohd

doi:10.1109/icsgrc.2010.5562519

Cited by 13 publications

(6 citation statements)

References 4 publications

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“…Gaussian noise [7] and speckle noise [8] which are used in used in ANFIS are examples of these challenges. Examples [9] and [10] illustrate the use of ANFIS in the field of cancer growth segmentation. Other de-noising methods are also reported in [11].…”

Section: Pre-processingmentioning

confidence: 99%

Big data simulation software for breast cancer growth repository system

Alias

Ghani²,

Saipo³

et al. 2018

IJET

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The development of the simulation software aims at anticipating the growth of breast cancer. Based on certain numerical iterative methods, this simulation works with discretization and Partial Differential Equation (PDE). As a class of Helmholtz equations, PDE approach are known to govern the growth of this type of cancer. Considering both time and place, the Helmholtz equation’s accuracy visualizes breast cancer and its growth. This growth is of breast cancer is captured and the convergence results in sequential and parallel computing environ-ment is expressed through the numerical libraries available in the repository system. Currently, both the parallel performance measurement and Numerical analysis that involve execution time, speedup, efficiency, effectiveness and temporal performance are being investigated. The process of breast cancer visualization requires a huge memory and expensive calculations. It is observed that both the distributed memory and distributed processors of the parallel computer systems development were required in most of the studies conducted on the growth of this cancer. It is considered as an important computation platform needed to the development of parallel repository system leading to an increase in the speed and a decrease in the cost. The simulation software has several beneficial characteristics such as high performance estimation, multidimensional visualization of breast cancer and being friendly. It also provides a real time solution and strength. This soft-ware is expected to increase the level of confidence in terms of computer-aided decision making which can be reflected positively on com-prehensive breast cancer screening; breast cancer diagnosis; and clinical assessments and treatment.

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Section: Pre-processingmentioning

confidence: 99%

Big data simulation software for breast cancer growth repository system

Alias

Ghani²,

Saipo³

et al. 2018

IJET

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“…Several techniques for medical image segmentation exist. These include methods such as particle swarm optimization [5], genetic algorithms [6] and artificial fuzzy logic [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)

et al. 2013

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In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures' results are less than 0.05, such as: relative overlap (p =0.0002), misclassification rate (p =0.045), true negative fraction (p =0.0001) and sum of true volume fraction (p =0.0001).

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“…ANFIS has been used in image processing by several researchers, some of them are for: image segmentation [1][2][3][4], extraction of images [5], and filtering noise in the image [6][7][8][9][10][11][12][13][14][15]. Advantages use of fuzzy systems and neural networks as a filter are because this method is based on the nonlinear approach features and this method is an adaptive function [7].…”

Section: Introductionmentioning

confidence: 99%

“…ANFIS architecture can be used to model employs a non-linear function and irregular, and can identify nonlinear components in the system [2]. From the block diagram of Figure 2, show that the use of data image in Lab color space (data input) and data image prediction (data output) as training data to build the FIS structure.…”

Section: Anfismentioning

confidence: 99%