Analysis of Magnetic Resonance Imaging of Brain Tumour using Fuzzy Set Rules
Introduction: Cancer becomes life threatening once the expansion of tissues in human brain turns into an uncontrolled growth. In detection of brain tumours, Magnetic Resonance Imaging (MRI) images give better results when compared to Computerised Tomography (CT) scan and X-ray. Malignant tumours...
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| Format: | Article |
| Language: | English |
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JCDR Research and Publications Private Limited
2021-12-01
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| Series: | Journal of Clinical and Diagnostic Research |
| Subjects: | |
| Online Access: | https://jcdr.net/articles/PDF/15770/52858_F_(SS)_PFA(SS)_PF1(PS_SC_SS)_PFA(KM_VJ_HJ_SL)_PN(KM).pdf |
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| author | Manini singh Vineeta Saxena Nigam |
| author_facet | Manini singh Vineeta Saxena Nigam |
| author_sort | Manini singh |
| collection | DOAJ |
| description | Introduction: Cancer becomes life threatening once the expansion
of tissues in human brain turns into an uncontrolled growth. In
detection of brain tumours, Magnetic Resonance Imaging (MRI)
images give better results when compared to Computerised
Tomography (CT) scan and X-ray. Malignant tumours can be
detected with the help of image processing and machine learning
techniques. These techniques detect even a small abnormality in
the human brain following a four-stage process which includes
preprocessing, segmentation, feature extraction and optimisation.
Aim: To predict a brain tumour using Fuzzy minimum-maximum
rule in MRI.
Materials and Methods: The medical challenge is how can we
rapidly and precisely diagnose brain lesions. It is difficult when
using standard image analysis to differentiate the benign and
malignant lesions. Hereby, authors have used a Fuzzy imaging
algorithm in data set of 253 brain tumour images of high grade
tumour colllected from kaggle.com.This paper proposes a fuzzy
min-max image processing algorithm. Image processing includes
four stages- preprocessing, segmentation, feature extraction
and accuracy detection. Brain tumours are located using various
algorithms at each of these stages.
Results: There were more than 20 features which can be taken
into consideration when using the Fuzzy image algorithm. The
proposed method in its current form achieves an accuracy of
about 95% by considering seven features.
Conclusion: The study revealed that age, shape, contour, blood
supply, capsule of tumour, oedema, post contrast enhancement,
cyst generation signal intensity of T-1 weighted image etc. were
the important investigation parameters. Location and size are
important to the domain experts, but due to their complexity
these parameters are not considered here. |
| first_indexed | 2024-04-11T20:02:34Z |
| format | Article |
| id | doaj.art-843a2329d7074954b1ca41e22089170c |
| institution | Directory Open Access Journal |
| issn | 2249-782X 0973-709X |
| language | English |
| last_indexed | 2024-04-11T20:02:34Z |
| publishDate | 2021-12-01 |
| publisher | JCDR Research and Publications Private Limited |
| record_format | Article |
| series | Journal of Clinical and Diagnostic Research |
| spelling | doaj.art-843a2329d7074954b1ca41e22089170c2022-12-22T04:05:32ZengJCDR Research and Publications Private LimitedJournal of Clinical and Diagnostic Research2249-782X0973-709X2021-12-011512061010.7860/JCDR/2021/52858.15770Analysis of Magnetic Resonance Imaging of Brain Tumour using Fuzzy Set RulesManini singh0Vineeta Saxena Nigam1Research Scholar, Department of Electronics and Communication, University Institute of Technology, RGPV, Bhopal, Madhya Pradesh, IndiaProfessor and Head, Department of Electronics and Communication, University Institute of Technology, RGPV, Bhopal, Madhya Pradesh, IndiaIntroduction: Cancer becomes life threatening once the expansion of tissues in human brain turns into an uncontrolled growth. In detection of brain tumours, Magnetic Resonance Imaging (MRI) images give better results when compared to Computerised Tomography (CT) scan and X-ray. Malignant tumours can be detected with the help of image processing and machine learning techniques. These techniques detect even a small abnormality in the human brain following a four-stage process which includes preprocessing, segmentation, feature extraction and optimisation. Aim: To predict a brain tumour using Fuzzy minimum-maximum rule in MRI. Materials and Methods: The medical challenge is how can we rapidly and precisely diagnose brain lesions. It is difficult when using standard image analysis to differentiate the benign and malignant lesions. Hereby, authors have used a Fuzzy imaging algorithm in data set of 253 brain tumour images of high grade tumour colllected from kaggle.com.This paper proposes a fuzzy min-max image processing algorithm. Image processing includes four stages- preprocessing, segmentation, feature extraction and accuracy detection. Brain tumours are located using various algorithms at each of these stages. Results: There were more than 20 features which can be taken into consideration when using the Fuzzy image algorithm. The proposed method in its current form achieves an accuracy of about 95% by considering seven features. Conclusion: The study revealed that age, shape, contour, blood supply, capsule of tumour, oedema, post contrast enhancement, cyst generation signal intensity of T-1 weighted image etc. were the important investigation parameters. Location and size are important to the domain experts, but due to their complexity these parameters are not considered here.https://jcdr.net/articles/PDF/15770/52858_F_(SS)_PFA(SS)_PF1(PS_SC_SS)_PFA(KM_VJ_HJ_SL)_PN(KM).pdfbrain lesionsfuzzy algorithmimage processingoedemasegmentation methods |
| spellingShingle | Manini singh Vineeta Saxena Nigam Analysis of Magnetic Resonance Imaging of Brain Tumour using Fuzzy Set Rules Journal of Clinical and Diagnostic Research brain lesions fuzzy algorithm image processing oedema segmentation methods |
| title | Analysis of Magnetic Resonance Imaging of Brain Tumour using Fuzzy Set Rules |
| title_full | Analysis of Magnetic Resonance Imaging of Brain Tumour using Fuzzy Set Rules |
| title_fullStr | Analysis of Magnetic Resonance Imaging of Brain Tumour using Fuzzy Set Rules |
| title_full_unstemmed | Analysis of Magnetic Resonance Imaging of Brain Tumour using Fuzzy Set Rules |
| title_short | Analysis of Magnetic Resonance Imaging of Brain Tumour using Fuzzy Set Rules |
| title_sort | analysis of magnetic resonance imaging of brain tumour using fuzzy set rules |
| topic | brain lesions fuzzy algorithm image processing oedema segmentation methods |
| url | https://jcdr.net/articles/PDF/15770/52858_F_(SS)_PFA(SS)_PF1(PS_SC_SS)_PFA(KM_VJ_HJ_SL)_PN(KM).pdf |
| work_keys_str_mv | AT maninisingh analysisofmagneticresonanceimagingofbraintumourusingfuzzysetrules AT vineetasaxenanigam analysisofmagneticresonanceimagingofbraintumourusingfuzzysetrules |