Name
Title | ||
|---|---|---|
Detection of the invasion of bladder tumor into adjacent wall based on textural features extracted from MRI images |
Abstract | ||
|---|---|---|
The invasion depth of a bladder tumor is of great importance for tumor staging and treatment planning. Considering that MRI bladder images could provide natural contrast between the urine and bladder wall, some texture features have been extracted from MRI images in our previous study, demonstrating a statistically significant difference between tumor tissues and wall tissues. In this study, a classification and labeling scheme has been proposed for the detection of the invasion depth of bladder tumors, based on these selected features, such as mean, standard deviation, uniformity, covariance, and contrast. Experimental results using patients' MRI datasets show the feasibility of the proposed scheme for labeling of bladder tumors, indicating its potential for noninvasive detection of bladder tumors and their stage. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-25719-3_10 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
mri bladder image,mri image,mri datasets,bladder wall,noninvasive detection,adjacent wall,tumor tissue,tumor staging,bladder tumor,natural contrast,invasion depth | Radiation treatment planning,Tumor Staging,Radiology,Standard deviation,Medicine,Pathology | Conference |
Volume | Issue | ISSN |
6668 LNCS | null | 16113349 |
Citations | PageRank | References |
2 | 0.48 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhide Wu | 1 | 2 | 0.48 |
| Zhengxing Shi | 2 | 2 | 0.81 |
| Guopeng Zhang | 3 | 70 | 6.14 |
| Hongbing Lu | 4 | 325 | 37.37 |