Fusion of Random Projection, Multi-Resolution Features and Distance Weighted K Nearest Neighbor for Masses Detection in Mammographic Images
Breast cancer is the top cancer in women both in the developed and the developing world. For early detection of the disease, mammography is still the most effective method beside ultrasound and magnetic resonance imaging. Computer Aided Detection systems have been developed to aid radiologists in di...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2018-06-01.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02331 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs12037_8466 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Nguyen, Viet Dung |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Le, Minh Dong |e author |
| 245 | 0 | 0 | |a Fusion of Random Projection, Multi-Resolution Features and Distance Weighted K Nearest Neighbor for Masses Detection in Mammographic Images |
| 260 | |b Institute of Advanced Engineering and Science, |c 2018-06-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/12037 | ||
| 520 | |a Breast cancer is the top cancer in women both in the developed and the developing world. For early detection of the disease, mammography is still the most effective method beside ultrasound and magnetic resonance imaging. Computer Aided Detection systems have been developed to aid radiologists in diagnosing breast cancer. Different methods were proposed to overcome the main drawback of producing large number of False Positives. In this paper, we presented a novel method for masses detection in mammograms. To describe masses, multi-resolution features were utilized. In feature extraction step, we calculated multi-resolution Block Difference Inverse Probability features and multi-resolution statistical features. Once the descriptors were extracted, we deployed random projection and distance weighted K Nearest Neighbor to classify the detected masses. The result is quite sanguine with sensitivity, false positive reduction and time for carrying out the algorithm | ||
| 540 | |a Copyright (c) 2018 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Mammography; Mass detection; Random projection; Multi-resolution features; Distance weighted K; Nearest neighbor | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 10, No 3: June 2018; 1030-1035 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v10.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/12037/8466 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/12037/8466 |z Get fulltext |