Name
Abstract | ||
|---|---|---|
Spleen segmentation is especially challenging as the majority of solid organs in the abdomen region have similar gray level range. Physician analysis of computed tomography (CT) images to assess abdominal trauma could be very time consuming and hence, automating this process can reduce time to treatment. The proposed method presented in this paper is a fully automated and knowledge based technique that employs anatomical information to accurately segment the spleen in CT images. The spleen detection procedure is proposed to locate the spleen in both healthy and injured cases. In the presence of hemorrhage and laceration, the edge merging technique is used. The accuracy of the method is measured by some criteria such as mis---segmented area, accuracy, specificity and sensitivity. The results show that the proposed spleen segmentation method performs well and outperforms other methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s10916-015-0271-x | Journal of Medical Systems |
Keywords | Field | DocType |
Spleen segmentation, Contrast enhancement, Edge detection, Seed growing, Traumatic abdominal injuries | Spleen,Abdominal trauma,Segmentation,Abdomen,Edge detection,Gray level,Computed tomography,Radiology,Merge (version control),Medicine | Journal |
Volume | Issue | ISSN |
39 | 9 | 1573-689X |
Citations | PageRank | References |
14 | 0.41 | 16 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. M. Reza Soroushmehr | 1 | 30 | 2.70 |
| Pavani Davuluri | 2 | 31 | 2.74 |
| Somayeh Molaei | 3 | 19 | 1.95 |
| Rosalyn Hobson Hargraves | 4 | 37 | 4.47 |
| Yang Tang | 5 | 24 | 1.97 |
| Charles H. Cockrell | 6 | 22 | 1.92 |
| K. Ward | 7 | 18 | 2.50 |
| Kayvan Najarian | 8 | 262 | 59.53 |