Lung Cancer Detection on CT Scan Images A Review on the Analysis Techniques
0. Introduction
1. Review of existing nodule detection methods
1.1 Pre_processing
1.2 Segmentation
- 2D-based segmentation
① Thresholding based methods[7][8][11][10][17][26][76][99]
② Stochastic methods[22][40]
③ Region based methods[1][18][20][28][59][62][63][79][80]
④ Contour based methods[9][44][47][49][77][82][95]
⑤ Learning based methods[4][19][45][58][90]
- 3D-based approaches
① Thresholding methods[91][92][96]
② Mathematical morphology[30][38][52][53][56][57][69]
③ Region-based methods
- Region growing[19][21][37][54][55]
- Graph-based methods(Graph-cut)[97][98]
④ Model-based methods(Deformable models)[14][25][24][29][47][48][83][93][258]
⑤ Dynamic programming[86][87]
1.3 Nodule extraction and classification
- Fuzzy classification and Neural Networks---[2][4][12][13][19][43][52][61][62][73][86]
- K-Nearest Neighbor---[25][50][66][81][96]
- Support vector machines---[36][46][65][70]
- Linear Discriminant Analysis---[5][33][41][49][67]
2. Conclusion
- Developing new and better techniques of contrast enhancement;
- selecting better criteria for performance evaluation is also needed.
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