Name
Title | ||
|---|---|---|
A fast weak-supervised pulmonary nodule segmentation method based on modified self-adaptive FCM algorithm. |
Abstract | ||
|---|---|---|
One of the key problems of computer-aided diagnosis is to segment specific anatomy structures in tomographic images as fast and accurately as possible, which is an important step toward identifying pathologically changed tissues. The segmentation accuracy has a significant impact on diseases diagnosis as well as the therapeutic efficacy. This paper presents a fast and robust weak-supervised pulmonary nodule segmentation method based on a modified self-adaptive FCM algorithm. To improve the traditional FCM, we firstly introduce an enhanced objective function, which computes the membership value according to both the grayscale similarity and spatial similarity between central pixels and neighbors. Then, a probability relation matrix between clusters and categories is constructed by using a small amount of prior knowledge learned from training samples. Based on this matrix, we realize a weak-supervised pulmonary nodules segmentation for unlabeled lung CT images. More specifically, the proposed method utilizes the relation matrix to calculate the category index of every pixel by Bayesian theory and PSOm algorithm. The quantitative experimental results on a test dataset, including 115 2-D clinical CT data, demonstrate the accuracy, efficiency and generality of the proposed weak-supervised strategy in pulmonary nodules segmentation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s00500-017-2608-5 | Soft Comput. |
Keywords | Field | DocType |
Modified self-adaptive FCM, Probability relation matrix, PSOm algorithm, Unlabeled CT images, Weak-supervised segmentation | Scale-space segmentation,Computer science,Matrix (mathematics),Artificial intelligence,Grayscale,Computer vision,Logical matrix,Pattern recognition,Segmentation,Algorithm,Self adaptive,Pixel,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
22 | 12 | 1432-7643 |
Citations | PageRank | References |
2 | 0.37 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Liu | 1 | 39 | 10.58 |
| Fenghuan Geng | 2 | 2 | 0.37 |
| Qiang Guo | 3 | 629 | 72.75 |
| Caiqing Zhang | 4 | 9 | 2.79 |
| Caiming Zhang | 5 | 446 | 88.19 |