Name
Title | ||
|---|---|---|
Tumor extraction and elimination of pectoral muscle based on hidden Markov and region growing: Applied based MIAS |
Abstract | ||
|---|---|---|
In this article, we propose an automatic method for the detection and extraction of the tumor on mammogram images. Most methods of detection of a tumor require the extraction of a large number of texture features from multiple calculations. The study first examines a technique of preprocessing images to obtain the Otsu thresholding method to eliminate items that do not belong in. After performing the thresholding, we estimate the number of base classes of technical LBP (Local Binary Pattern). To automate the initialization task, the classification proposed by applying dynamic k-means and improve the classes obtained by the method of Markov. Then we calculate the correlation between these classes and the original image, we deduce the class that contains the tumor and muscle pectoral. Finally, it uses the method of growing the region to eliminate pectoral muscle. The result obtained by this approach shows the quality and accuracy of extracting parts of the tumor compared to existing approaches in the literature. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ATSIP.2017.8075583 | 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) |
Keywords | Field | DocType |
Classification,tumors,mammogram image,Otsu thresholding,LBP (Local Binary Pattern),k-means,Markov | Computer vision,Pattern recognition,Computer science,Markov chain,Local binary patterns,Feature extraction,Preprocessor,Region growing,Artificial intelligence,Initialization,Thresholding,Hidden Markov model | Conference |
ISBN | Citations | PageRank |
978-1-5386-0552-3 | 1 | 0.36 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soukaina El Idrissi El Kaitouni | 1 | 1 | 0.36 |
| Abdelghafour Abbad | 2 | 4 | 1.75 |
| Hamid Tairi | 3 | 57 | 17.49 |