An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI
Brain tumors are a serious and death-defying disease for human life. Discovering an appropriate brain tumor image from a magnetic resonance imaging (MRI) archive is a challenging job for the radiologist. Most search engines retrieve images on the basis of traditional text-based approaches. The main...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9521518/ |
| _version_ | 1819125696301105152 |
|---|---|
| author | K. Venkatachalam Siuly Siuly Nebojsa Bacanin Stepan Hubalovsky Pavel Trojovsky |
| author_facet | K. Venkatachalam Siuly Siuly Nebojsa Bacanin Stepan Hubalovsky Pavel Trojovsky |
| author_sort | K. Venkatachalam |
| collection | DOAJ |
| description | Brain tumors are a serious and death-defying disease for human life. Discovering an appropriate brain tumor image from a magnetic resonance imaging (MRI) archive is a challenging job for the radiologist. Most search engines retrieve images on the basis of traditional text-based approaches. The main challenge in the MRI image analysis is that low-level visual information captured by the MRI machine and the high-level information identified by the assessor. This semantic gap is addressed in this study by designing a new feature extraction technique. In this paper, we introduce Content-Based Medical Image retrieval (CBMIR) system for retrieval of brain tumor images from the large data. Firstly, we remove noise from MRI images employing several filtering techniques. Afterward, we design a feature extraction scheme combining Gabor filtering technique (which is mainly focused on specific frequency content at the image region) and Walsh-Hadamard transform (WHT) (conquer technique for easy configuration of image) for discovering representative features from MRI images. After that, for retrieving the accurate and reliable image, we employ Fuzzy C-Means clustering Minkowski distance metric that can evaluate the similarity between the query image and database images. The proposed methodology design was tested on a publicly available brain tumor MRI image database. The experimental results demonstrate that our proposed approach outperforms most of the existing techniques like Gabor, wavelet, and Hough transform in detecting brain tumors and also take less time. The proposed approach will be beneficial for radiologists and also for technologists to build an automatic decision support system that will produce reproducible and objective results with high accuracy. |
| first_indexed | 2024-12-22T07:44:14Z |
| format | Article |
| id | doaj.art-b06eaf36c794485a872798a150feefa5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T07:44:14Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b06eaf36c794485a872798a150feefa52022-12-21T18:33:40ZengIEEEIEEE Access2169-35362021-01-01911907811908910.1109/ACCESS.2021.31073719521518An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRIK. Venkatachalam0Siuly Siuly1https://orcid.org/0000-0003-2491-0546Nebojsa Bacanin2https://orcid.org/0000-0002-2062-924XStepan Hubalovsky3Pavel Trojovsky4https://orcid.org/0000-0001-8992-125XDepartment of Applied Cybernetics, Faculty of Science, University of Hradec Králové, Hradec Králové, Czech RepublicInstitute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, VIC, AustraliaDepartment of Informatics and Computing, Singidunum University, Belgrade, SerbiaDepartment of Applied Cybernetics, Faculty of Science, University of Hradec Králové, Hradec Králové, Czech RepublicDepartment of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Králové, Czech RepublicBrain tumors are a serious and death-defying disease for human life. Discovering an appropriate brain tumor image from a magnetic resonance imaging (MRI) archive is a challenging job for the radiologist. Most search engines retrieve images on the basis of traditional text-based approaches. The main challenge in the MRI image analysis is that low-level visual information captured by the MRI machine and the high-level information identified by the assessor. This semantic gap is addressed in this study by designing a new feature extraction technique. In this paper, we introduce Content-Based Medical Image retrieval (CBMIR) system for retrieval of brain tumor images from the large data. Firstly, we remove noise from MRI images employing several filtering techniques. Afterward, we design a feature extraction scheme combining Gabor filtering technique (which is mainly focused on specific frequency content at the image region) and Walsh-Hadamard transform (WHT) (conquer technique for easy configuration of image) for discovering representative features from MRI images. After that, for retrieving the accurate and reliable image, we employ Fuzzy C-Means clustering Minkowski distance metric that can evaluate the similarity between the query image and database images. The proposed methodology design was tested on a publicly available brain tumor MRI image database. The experimental results demonstrate that our proposed approach outperforms most of the existing techniques like Gabor, wavelet, and Hough transform in detecting brain tumors and also take less time. The proposed approach will be beneficial for radiologists and also for technologists to build an automatic decision support system that will produce reproducible and objective results with high accuracy.https://ieeexplore.ieee.org/document/9521518/Hough filterGabor filterglioma brain tumoursoft computing techniquesWalsh-Hadamard transform |
| spellingShingle | K. Venkatachalam Siuly Siuly Nebojsa Bacanin Stepan Hubalovsky Pavel Trojovsky An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI IEEE Access Hough filter Gabor filter glioma brain tumour soft computing techniques Walsh-Hadamard transform |
| title | An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI |
| title_full | An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI |
| title_fullStr | An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI |
| title_full_unstemmed | An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI |
| title_short | An Efficient Gabor Walsh-Hadamard Transform Based Approach for Retrieving Brain Tumor Images From MRI |
| title_sort | efficient gabor walsh hadamard transform based approach for retrieving brain tumor images from mri |
| topic | Hough filter Gabor filter glioma brain tumour soft computing techniques Walsh-Hadamard transform |
| url | https://ieeexplore.ieee.org/document/9521518/ |
| work_keys_str_mv | AT kvenkatachalam anefficientgaborwalshhadamardtransformbasedapproachforretrievingbraintumorimagesfrommri AT siulysiuly anefficientgaborwalshhadamardtransformbasedapproachforretrievingbraintumorimagesfrommri AT nebojsabacanin anefficientgaborwalshhadamardtransformbasedapproachforretrievingbraintumorimagesfrommri AT stepanhubalovsky anefficientgaborwalshhadamardtransformbasedapproachforretrievingbraintumorimagesfrommri AT paveltrojovsky anefficientgaborwalshhadamardtransformbasedapproachforretrievingbraintumorimagesfrommri AT kvenkatachalam efficientgaborwalshhadamardtransformbasedapproachforretrievingbraintumorimagesfrommri AT siulysiuly efficientgaborwalshhadamardtransformbasedapproachforretrievingbraintumorimagesfrommri AT nebojsabacanin efficientgaborwalshhadamardtransformbasedapproachforretrievingbraintumorimagesfrommri AT stepanhubalovsky efficientgaborwalshhadamardtransformbasedapproachforretrievingbraintumorimagesfrommri AT paveltrojovsky efficientgaborwalshhadamardtransformbasedapproachforretrievingbraintumorimagesfrommri |