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Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach

Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach,10.1016/j.asoc.2009.11.019,Applied Soft Computing,Moha

Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach   (Citations: 4)
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This paper presents a systematic Type-II fuzzy expert system for diagnosing the human brain tumors (Astrocytoma tumors) using T1-weighted Magnetic Resonance Images with contrast. The proposed Type-II fuzzy image processing method has four distinct modules: Pre-processing, Segmentation, Feature Extraction, and Approximate Reasoning. We develop a fuzzy rule base by aggregating the existing filtering methods for Pre-processing step. For Segmentation step, we extend the Possibilistic C-Mean (PCM) method by using the Type-II fuzzy concepts, Mahalanobis distance, and Kwon validity index. Feature Extraction is done by Thresholding method. Finally, we develop a Type-II Approximate Reasoning method to recognize the tumor grade in brain MRI. The proposed Type-II expert system has been tested and validated to show its accuracy in the real world. The results show that the proposed system is superior in recognizing the brain tumor and its grade than Type-I fuzzy expert systems.
Journal: Applied Soft Computing - ASC , vol. 11, no. 1, pp. 285-294, 2011
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    • ...Many semi-automated and automated methods have been reported in the literature for identifying and segmenting brain lesions based on statistical and textural features in MRI [9-12, 18, 21-22]...
    • ...Automated segmentation using fuzzy system and deformable model was performed by Khotanloua et al. [11] and Fazel et al. [12] to detect tumour...

    Norhashimah Mohd Saadet al. Brain lesion segmentation of Diffusion-weighted MRI using gray level c...

    • ...Type-II fuzzy set, on the other hand, is able to successfully model these uncertainties [17]...
    • ...<{[SECTION]}>Figure 1: (a) Interval Valued Type-II (b) Generalized Type-II [17]...
    • ...In the literature, there are many methods for obtaining membership functions such as Heuristic Selection, Elicitation, Histogram, Genetic Algorithms, Neural Network, Clustering, Projection and Curve Fitting [17]...

    Y. Shafahiet al. Type-II fuzzy route choice modeling

    • ...There are many image processing methods in literature ([2], [3], [4], [5], [6], [7], [8], and [9])...
    • ...The goal of this paper is to improve the Type-2 fuzzy image processing expert system proposed by [2] by using Type-2 fuzzy function strategy...
    • ...Section V presents the approach of fuzzy image processing proposed by [2]...
    • ...tumor). Therefore, advanced image analysis techniques such as fuzzy segmentation methods are developed to optimally use the MRI data [2]...
    • ...On the other hand, Type-2 fuzzy membership values are themselves fuzzy and have the potential to provide better performance in diagnosing brain tumors based on image processing approaches [2]...
    • ...Based on the above information, [2] proposed a powerful image processing system, which can help the physicians with better diagnosis of human brain tumors and has impressing results in this field...
    • ...Considering the T1-weighted MR Images as an input data, the image processing approach proposed by [2] has four main steps, Pre-processing, Segmentation, Feature extraction, and Approximate reasoning (Fig. 2)...
    • ...<{[SECTION]}>Fig. 2. Image Processing Algorithm [2]...
    • ...The strategy of Type-2 fuzzy image processing algorithm proposed by [2] is as follows:...
    • ...By using this rule base, the noises are reduced, the edges are sharpened, and the overall result is much better than using one filter alone [2]...
    • ...Step2- Segmentation: By using the Type-2 PCM method (11-14) proposed by [2] and [36], the improved image is segmented into four classes (White Matter (WM), Grey Matter (GM), Cerebrospinal Fluid (CSF), and Abnormality)...
    • ...A Type-2 Kwon validity index (16) is used in order to obtain suitable Type-2 membership functions and to validate the Type-2 PCM algorithm [2]...
    • ...After extracting the features, the Thresholding method is used to recognize the characteristics of pixels values belong to these two clusters [2]...
    • ...By considering the results of Rule-3, Rule-4, and Patient's age, other rules are defined [2]...
    • ...The proposed image processing approach is based on [2] approach, which is shortly described in section V. As mentioned before, this approach has four main steps, Preprocessing, Segmentation, Feature extraction, and Approximate reasoning...
    • ...The parameters of Type-2 membership functions for existence of Mass Effect (Similarity and Dilation) and Tumor Shape (Cystic or Mass) are obtained by using the method proposed by [2] with the following steps:...
    • ...This is done by using 5 patient’s MR Images (similar to Fig. 3) [2]...
    • ..., , an interval Type-2 fuzzy is designed so that the error function ( ij e ) (17) is minimized [2]...
    • ...These parameters are tuned using steepest descent method [2]...
    • ...Step 1-3- Defining the suitable Type-2 membership functions: the initial membership functions, which are determined in the previous step, are used in the proposed Type-2 PCM method proposed by [2] and [36]...
    • ...The expert’s knowledge, which is used for this step, is as follows [2]:...
    • ...To compare the performance of Type-2 fuzzy rule-base with the proposed Type-2 fuzzy function, we used two different inference systems, Type-2 rule-base system based on [2], and the proposed Type-2 fuzzy function...

    M. H. Fazel Zarandiet al. Using Type2 fuzzy function for diagnosing brain tumors based on image ...

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