Name
Abstract | ||
|---|---|---|
Manual segmentation of micro-calcifications in mammogram can provide clinicians with useful information, such as an estimation of the quantification and the size of abnormalities. However, it is a time and labour consuming process. Automatic segmentation has the potential to assist both in the diagnosis of the disease and in treatment planning. This paper presents a novel mammogram image segmentation algorithm that makes use of Scale Invariant Feature Transform (SIFT) to compute the key point in the suspicious area of the mammograms. A database from MIAS is used in this approach. Initial results are presented to show that SIFT can be used to by computing the key-points to segment micro-calcifications of the mammograms. Further work will focus on finding the ways to set the threshold of the segmentation automatically. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/BMEI.2008.198 | BMEI (2) |
Keywords | Field | DocType |
initial result,suspicious area,automatic segmentation,treatment planning,segment micro-calcifications,labour consuming process,key point,novel mammogram image segmentation,manual segmentation,scale invariant feature transform,sift,cancer,biomedical engineering,image segmentation | Scale-invariant feature transform,Computer vision,Mammography,Scale-space segmentation,Pattern recognition,Microcalcification,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Image segmentation algorithm | Conference |
ISSN | Citations | PageRank |
1948-2914 | 4 | 0.46 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiu Guan | 1 | 43 | 9.92 |
| Jianhua Zhang | 2 | 25 | 5.97 |
| Sheng-Yong Chen | 3 | 1077 | 114.06 |
| Andrew Todd-Pokropek | 4 | 130 | 34.54 |