Name
Title | ||
|---|---|---|
Regression as a Tool to Measure Segmentation Quality and Preliminary Indicator of Diseased Lungs. |
Abstract | ||
|---|---|---|
Segmentation of the lung from HRCT Thorax images was studied. An automatic method of determining segmentation area is proposed. High quality of segmentation is considered achieved when the segmented area from the proposed algorithm is almost identical to the area obtained from the manual tracings by lung expert ground truth. High correlation between the two types of segmented areas showed that regression may be used as a tool to measure segmentation quality. Supplementary information may also be obtained from the regression plot. Prediction interval may be used as a possible indicator of diseased whilst outliers may show or indicate low segmentation quality or a possible severity of the disease. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-29451-3_40 | PSIVT |
Keywords | Field | DocType |
Lung segmentation, Regression, Segmentation quality, High resolution computed tomography | Computer vision,Scale-space segmentation,Pattern recognition,Regression,Computer science,Segmentation,Outlier,Ground truth,Correlation,Prediction interval,Artificial intelligence,Lung segmentation | Conference |
Volume | ISSN | Citations |
9431 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Norliza Mohd. Noor | 1 | 37 | 9.25 |
| M. Omar | 2 | 42 | 7.77 |
| Joel C. Than | 3 | 26 | 2.52 |
| Rosminah M. Kassim | 4 | 26 | 2.52 |
| Ashari Yunus | 5 | 30 | 3.68 |