Research Article
Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network
Table 1
Comparative study of the method proposed with its limitation.
| Author | Method | Limitation |
|
Bert et al. (2009) [5] | Automatic segmentation of colon using 3D seeded region growing algorithm | It is obvious that the most serious problem of region growing is the power and time consuming |
| LosnegÄrd et al. (2010) [7] | Semiautomatic segmentation | Disadvantage is that it consumes more time and lesser accuracy |
| Lu and Zhao (2011) [8] | Noncolonic attachment classification algorithm and a heuristic connection algorithm | This method could achieve 92.86% coverage of human-generated colons, which is of 13.68% higher than the conventional method |
| Chowdhury and Whelan (2011) [9] | Automatic colon segmentation from CT data using colon geometrical features | This approach performs better and provides efficient results in colon segmentation |
| Kilic et al. (2009) [12] | Automatic three-dimensional computer-aided detection system | Average coverage is about 87.5% of the entire colon |
| Taimouri et al. (2011) [10] | Constrained least-squares filtering (CLSF) | Applicable only for specific cases, not converged early |
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