An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering
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...
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/18/7816 |
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| author | Surjeet Dalal Umesh Kumar Lilhore Poongodi Manoharan Uma Rani Fadl Dahan Fahima Hajjej Ismail Keshta Ashish Sharma Sarita Simaiya Kaamran Raahemifar |
| author_facet | Surjeet Dalal Umesh Kumar Lilhore Poongodi Manoharan Uma Rani Fadl Dahan Fahima Hajjej Ismail Keshta Ashish Sharma Sarita Simaiya Kaamran Raahemifar |
| author_sort | Surjeet Dalal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:02:45Z |
| format | Article |
| id | doaj.art-d7047e626c044bbead31fcc0b7c1f5e8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:02:45Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d7047e626c044bbead31fcc0b7c1f5e82023-11-19T12:54:37ZengMDPI AGSensors1424-82202023-09-012318781610.3390/s23187816An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean ClusteringSurjeet Dalal0Umesh Kumar Lilhore1Poongodi Manoharan2Uma Rani3Fadl Dahan4Fahima Hajjej5Ismail Keshta6Ashish Sharma7Sarita Simaiya8Kaamran Raahemifar9Department of Computer Science and Engineering, Amity University Gurugram, Gurugram 122412, Haryana, IndiaDepartment of Computer Science and Engineering, Chandigarh University, Mohali 140413, Punjab, IndiaCollege of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha P.O. Box 5825, QatarDepartment of Computer Science and Engineering, World College of Technology & Management, Gurugram 122413, Haryana, IndiaDepartment of Management Information Systems, College of Business Administration Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi ArabiaComputer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh 13713, Saudi ArabiaDepartment of Computer Engineering and Applications, GLA University, Mathura 281406, Uttar Pradesh, IndiaApex Institute of Technology (CSE), Chandigarh University, Gharuan, Mohali 140413, Punjab, IndiaData Science and Artificial Intelligence Program, College of Information Sciences and Technology, Penn State University, State College, PS 16801, USABrain 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.https://www.mdpi.com/1424-8220/23/18/7816brain tumoradaptive self-organizing mapK-meansgray level co gray level co-occurrence matrixmedical imaging |
| spellingShingle | Surjeet Dalal Umesh Kumar Lilhore Poongodi Manoharan Uma Rani Fadl Dahan Fahima Hajjej Ismail Keshta Ashish Sharma Sarita Simaiya Kaamran Raahemifar An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering Sensors brain tumor adaptive self-organizing map K-means gray level co gray level co-occurrence matrix medical imaging |
| 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 | efficient brain tumor segmentation method based on adaptive moving self organizing map and fuzzy k mean clustering |
| topic | brain tumor adaptive self-organizing map K-means gray level co gray level co-occurrence matrix medical imaging |
| url | https://www.mdpi.com/1424-8220/23/18/7816 |
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