Sign in
Author

Conference

Journal

Organization

Year

DOI
Look for results that meet for the following criteria:
since
equal to
before
between
and
Search in all fields of study
Limit my searches in the following fields of study
Agriculture Science
Arts & Humanities
Biology
Chemistry
Computer Science
Economics & Business
Engineering
Environmental Sciences
Geosciences
Material Science
Mathematics
Medicine
Physics
Social Science
Multidisciplinary
Keywords
(14)
Approximate Reasoning
Brain Tumor
Feature Extraction
Fuzzy Expert System
Fuzzy Image Processing
Fuzzy Logic
Fuzzy Rule Base
Human Brain
Image Processing
Indexation
Magnetic Resonance Image
mahalanobis distance
Expert System
possibilistic c means
Subscribe
Academic
Publications
Systematic image processing for diagnosing brain tumors: A TypeII fuzzy expert system approach
Systematic image processing for diagnosing brain tumors: A TypeII fuzzy expert system approach,10.1016/j.asoc.2009.11.019,Applied Soft Computing,Moha
Edit
Systematic image processing for diagnosing brain tumors: A TypeII fuzzy expert system approach
(
Citations: 4
)
BibTex

RIS

RefWorks
Download
Mohammad Hossein Fazel Zarandi
,
Marzie Zarinbal
,
M. Izadi
This paper presents a systematic TypeII
fuzzy expert system
for diagnosing the
human brain
tumors (Astrocytoma tumors) using T1weighted
Magnetic Resonance
Images with contrast. The proposed TypeII
fuzzy image processing
method has four distinct modules: Preprocessing, Segmentation, Feature Extraction, and Approximate Reasoning. We develop a
fuzzy rule base
by aggregating the existing filtering methods for Preprocessing step. For Segmentation step, we extend the Possibilistic CMean (PCM) method by using the TypeII fuzzy concepts, Mahalanobis distance, and Kwon validity index.
Feature Extraction
is done by Thresholding method. Finally, we develop a TypeII
Approximate Reasoning
method to recognize the tumor grade in brain MRI. The proposed TypeII
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 TypeI fuzzy expert systems.
Journal:
Applied Soft Computing  ASC
, vol. 11, no. 1, pp. 285294, 2011
DOI:
10.1016/j.asoc.2009.11.019
Cumulative
Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
(
www.sciencedirect.com
)
(
dx.doi.org
)
(
www.informatik.unitrier.de
)
Citation Context
(3)
...Many semiautomated and automated methods have been reported in the literature for identifying and segmenting brain lesions based on statistical and textural features in MRI [
912
, 18, 2122]...
...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 Saad
,
et al.
Brain lesion segmentation of Diffusionweighted MRI using gray level c...
...TypeII fuzzy set, on the other hand, is able to successfully model these uncertainties [
17
]...
...<{[SECTION]}>Figure 1: (a) Interval Valued TypeII (b) Generalized TypeII [
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. Shafahi
,
et al.
TypeII 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 Type2 fuzzy image processing expert system proposed by [
2
] by using Type2 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, Type2 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 T1weighted MR Images as an input data, the image processing approach proposed by [
2
] has four main steps, Preprocessing, Segmentation, Feature extraction, and Approximate reasoning (Fig. 2)...
...<{[SECTION]}>Fig. 2. Image Processing Algorithm [
2
]...
...The strategy of Type2 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 Type2 PCM method (1114) 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 Type2 Kwon validity index (16) is used in order to obtain suitable Type2 membership functions and to validate the Type2 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 Rule3, Rule4, 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 Type2 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 Type2 fuzzy is designed so that the error function ( ij e ) (17) is minimized [
2
]...
...These parameters are tuned using steepest descent method [
2
]...
...Step 13 Defining the suitable Type2 membership functions: the initial membership functions, which are determined in the previous step, are used in the proposed Type2 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 Type2 fuzzy rulebase with the proposed Type2 fuzzy function, we used two different inference systems, Type2 rulebase system based on [
2
], and the proposed Type2 fuzzy function...
M. H. Fazel Zarandi
,
et al.
Using Type2 fuzzy function for diagnosing brain tumors based on image ...
References
(36)
Fuzzy rough sets hybrid scheme for breast cancer detection
(
Citations: 38
)
Aboul Ella Hassanien
Journal:
Image and Vision Computing  IVC
, vol. 25, no. 2, pp. 172183, 2007
Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images
(
Citations: 30
)
Alan WeeChung Liew
,
Hong Yan
Journal:
Current Medical Imaging Reviews  CURR MED IMAGING REV
, vol. 2, no. 1, pp. 91103, 2006
Toward breast cancer diagnosis based on automated segmentation of masses in mammograms
(
Citations: 12
)
Alfonso Rojas Domínguez
,
Asoke K. Nandi
Journal:
Pattern Recognition  PR
, vol. 42, no. 6, pp. 11381148, 2009
Noise reduction by fuzzy image filtering
(
Citations: 58
)
Dimitri Van De Ville
,
Mike Nachtegael
,
Dietrich Van der Weken
,
Etienne E. Kerre
,
Wilfried Philips
,
Ignace Lemahieu
Journal:
IEEE Transactions on Fuzzy Systems  TFS
, vol. 11, no. 4, pp. 429436, 2003
A new unsupervised approach for fuzzy clustering
(
Citations: 13
)
Efendi N. Nasibov
,
Gözde Ulutagay
Journal:
Fuzzy Sets and Systems  FSS
, vol. 158, no. 19, pp. 21182133, 2007
Sort by:
Citations
(4)
Brain lesion segmentation of Diffusionweighted MRI using gray level cooccurrence matrix
Norhashimah Mohd Saad
,
S. A. R. AbuBakar
,
Sobri Muda
,
M. M. Mokji
,
Lizawati Salahuddin
Conference:
IEEE International Workshop on Imaging Systems and Techniques  IST
, 2011
TypeII fuzzy route choice modeling
Y. Shafahi
,
A. Zarinbal Masouleh
,
M. Zarinbal Masouleh
Conference:
Conference of the North American Fuzzy Information Processing Society  NAFIPS
, 2010
Using Type2 fuzzy function for diagnosing brain tumors based on image processing approach
M. H. Fazel Zarandi
,
M. Zarinbal
,
A. Zarinbal
,
I. B. Turksen
,
M. Izadi
Conference:
IEEE International Conference on Fuzzy Systems
, pp. 18, 2010
Singlemode thuliumdoped silica fiber laser pumped at 1230 nm with 7W of output power
I. A. Bufetov
,
K. S. Kravtsov
,
O. I. Medvedkov
,
E. M. Dianov
Journal:
Information Sciences  ISCI
, 2005