Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization
The automatic brain tumor segmentation in MRI (Magnetic Resonance Images) is becoming a challenging task in the field of medicine, since the brain tumor occurs in different shapes, intensities and sizes. This paper proposes an efficient automatic brain tumor segmentation using Greedy Snake Model and...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-03-01
|
| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157818313120 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849315727179251712 |
|---|---|
| author | C. Jaspin Jeba Sheela G. Suganthi |
| author_facet | C. Jaspin Jeba Sheela G. Suganthi |
| author_sort | C. Jaspin Jeba Sheela |
| collection | DOAJ |
| description | The automatic brain tumor segmentation in MRI (Magnetic Resonance Images) is becoming a challenging task in the field of medicine, since the brain tumor occurs in different shapes, intensities and sizes. This paper proposes an efficient automatic brain tumor segmentation using Greedy Snake Model and Fuzzy C-Means optimization. This method initially identifies the approximate Region Of Interest (ROI), by removing the non-tumor part by two level morphological reconstruction such as dilation and erosion. A mask is formed by thresholding the reconstructed image and is eroded to improve the accuracy of segmentation in Greedy Snake algorithm. Using the mask boundary as initial contour of the snake, the greedy snake model estimates the new boundaries of tumor. These boundaries are accurate in regions where there is sharp edge and are less accurate where there are ramp edges. The inaccurate boundaries are further optimized by using Fuzzy C-Means algorithm to obtain the accurate segmentation output. The region that has large perimeter is finally chosen, to eliminate the in- accurate segmented regions. The experimental verification were done on T1-weighted contrast-enhanced image data set, using the metrics such as dice score, specificity, sensitivity and Hausdorff distance. The proposed method outperforms when compared with the traditional brain tumor segmentation methods in MRI images. |
| format | Article |
| id | doaj-art-60ae33cfe4884e6ab64fc26ec70e66e8 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-60ae33cfe4884e6ab64fc26ec70e66e82025-08-20T03:52:03ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-03-0134355756610.1016/j.jksuci.2019.04.006Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means OptimizationC. Jaspin Jeba Sheela0G. Suganthi1Reg.No 17221282162010, St. Xaviers Autonomous College, Palayamkotai affiliated to Manonmaniam Sunadaranar University, Abishekapatti, Tirunelveli 627012, Tamil Nadu, India; Corresponding author.Department of Computer Science, W.C College, Nagercoil affiliated to Manonmaniam Sunadaranar University, Abishekapatti, Tirunelveli 627012, Tamil Nadu, IndiaThe automatic brain tumor segmentation in MRI (Magnetic Resonance Images) is becoming a challenging task in the field of medicine, since the brain tumor occurs in different shapes, intensities and sizes. This paper proposes an efficient automatic brain tumor segmentation using Greedy Snake Model and Fuzzy C-Means optimization. This method initially identifies the approximate Region Of Interest (ROI), by removing the non-tumor part by two level morphological reconstruction such as dilation and erosion. A mask is formed by thresholding the reconstructed image and is eroded to improve the accuracy of segmentation in Greedy Snake algorithm. Using the mask boundary as initial contour of the snake, the greedy snake model estimates the new boundaries of tumor. These boundaries are accurate in regions where there is sharp edge and are less accurate where there are ramp edges. The inaccurate boundaries are further optimized by using Fuzzy C-Means algorithm to obtain the accurate segmentation output. The region that has large perimeter is finally chosen, to eliminate the in- accurate segmented regions. The experimental verification were done on T1-weighted contrast-enhanced image data set, using the metrics such as dice score, specificity, sensitivity and Hausdorff distance. The proposed method outperforms when compared with the traditional brain tumor segmentation methods in MRI images.http://www.sciencedirect.com/science/article/pii/S1319157818313120MRI imagesImage segmentationFuzzy C-means algorithmGreedy snake model |
| spellingShingle | C. Jaspin Jeba Sheela G. Suganthi Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization Journal of King Saud University: Computer and Information Sciences MRI images Image segmentation Fuzzy C-means algorithm Greedy snake model |
| title | Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization |
| title_full | Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization |
| title_fullStr | Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization |
| title_full_unstemmed | Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization |
| title_short | Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization |
| title_sort | automatic brain tumor segmentation from mri using greedy snake model and fuzzy c means optimization |
| topic | MRI images Image segmentation Fuzzy C-means algorithm Greedy snake model |
| url | http://www.sciencedirect.com/science/article/pii/S1319157818313120 |
| work_keys_str_mv | AT cjaspinjebasheela automaticbraintumorsegmentationfrommriusinggreedysnakemodelandfuzzycmeansoptimization AT gsuganthi automaticbraintumorsegmentationfrommriusinggreedysnakemodelandfuzzycmeansoptimization |