An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering
<p dir="ltr">Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumo...
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2023
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| _version_ | 1864513507126411264 |
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| author | Surjeet Dalal (4906894) |
| author2 | Umesh Kumar Lilhore (17727684) Poongodi Manoharan (17727687) Uma Rani (19482358) Fadl Dahan (19482361) Fahima Hajjej (11675462) Ismail Keshta (17727699) Ashish Sharma (319838) Sarita Simaiya (17727693) Kaamran Raahemifar (707645) |
| author2_role | author author author author author author author author author |
| author_facet | Surjeet Dalal (4906894) Umesh Kumar Lilhore (17727684) Poongodi Manoharan (17727687) Uma Rani (19482358) Fadl Dahan (19482361) Fahima Hajjej (11675462) Ismail Keshta (17727699) Ashish Sharma (319838) Sarita Simaiya (17727693) Kaamran Raahemifar (707645) |
| author_role | author |
| dc.creator.none.fl_str_mv | Surjeet Dalal (4906894) Umesh Kumar Lilhore (17727684) Poongodi Manoharan (17727687) Uma Rani (19482358) Fadl Dahan (19482361) Fahima Hajjej (11675462) Ismail Keshta (17727699) Ashish Sharma (319838) Sarita Simaiya (17727693) Kaamran Raahemifar (707645) |
| dc.date.none.fl_str_mv | 2023-09-12T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s23187816 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_Efficient_Brain_Tumor_Segmentation_Method_Based_on_Adaptive_Moving_Self-Organizing_Map_and_Fuzzy_K-Mean_Clustering/26830171 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning brain tumor adaptive self-organizing map K-means gray level co gray level co-occurrence matrix medical imaging |
| dc.title.none.fl_str_mv | An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s23187816" target="_blank">https://dx.doi.org/10.3390/s23187816</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_2e2e60ee6c4bd9e04bf481cb44b923ab |
| identifier_str_mv | 10.3390/s23187816 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26830171 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean ClusteringSurjeet Dalal (4906894)Umesh Kumar Lilhore (17727684)Poongodi Manoharan (17727687)Uma Rani (19482358)Fadl Dahan (19482361)Fahima Hajjej (11675462)Ismail Keshta (17727699)Ashish Sharma (319838)Sarita Simaiya (17727693)Kaamran Raahemifar (707645)Biomedical and clinical sciencesOncology and carcinogenesisHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningbrain tumoradaptive self-organizing mapK-meansgray level co gray level co-occurrence matrixmedical imaging<p dir="ltr">Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s23187816" target="_blank">https://dx.doi.org/10.3390/s23187816</a></p>2023-09-12T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s23187816https://figshare.com/articles/journal_contribution/An_Efficient_Brain_Tumor_Segmentation_Method_Based_on_Adaptive_Moving_Self-Organizing_Map_and_Fuzzy_K-Mean_Clustering/26830171CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268301712023-09-12T09:00:00Z |
| spellingShingle | An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering Surjeet Dalal (4906894) Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning brain tumor adaptive self-organizing map K-means gray level co gray level co-occurrence matrix medical imaging |
| status_str | publishedVersion |
| title | An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering |
| title_full | An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering |
| title_fullStr | An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering |
| title_full_unstemmed | An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering |
| title_short | An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering |
| title_sort | An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering |
| topic | Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning brain tumor adaptive self-organizing map K-means gray level co gray level co-occurrence matrix medical imaging |