Name
Title | ||
|---|---|---|
Segmentation of multimodality osteosarcoma MRI with vectorial fuzzy-connectedness theory |
Abstract | ||
|---|---|---|
This paper illustrates an algorithm for osteosarcoma segmentation, using vectorial fuzzy-connectedness segmentation, and coming up with a methodology which can be used to segment some distinct tissues of osteosarcoma such as tumor, necrosis and parosteal sarcoma from 3D vectorial images. However, fuzzy-connectedness segmentation can be successfully used only in connected regions. In this paper, some improvements have been made to segment the interested tissues which are distributed in disconnected regions. And the paper speeds up the process of segmentation by segmenting two osteosarcoma tissues simultaneously. The methology has been applied to a medical image analysis system of osteosarcoma segmentation and 3D reconstruction, which has been put into practical use in some hospitals. © Springer-Verlag Berlin Heidelberg 2005. |
Year | DOI | Venue |
|---|---|---|
2005 | null | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
null | Iterative reconstruction,Computer vision,Market segmentation,Pattern recognition,Computer science,Segmentation,Fuzzy logic,Image processing,Image segmentation,Artificial intelligence,Sarcoma,3D reconstruction | Conference |
Volume | Issue | ISSN |
3614 LNAI | null | 16113349 |
ISBN | Citations | PageRank |
3-540-28331-5 | 5 | 0.54 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ma Jing | 1 | 5 | 0.54 |
| Ming-lu Li | 2 | 2584 | 235.94 |
| Yongqiang Zhao | 3 | 5 | 0.54 |
| J Ma | 4 | 14 | 2.31 |
| ML Li | 5 | 59 | 2.58 |
| YQ Zhao | 6 | 5 | 0.54 |