Name
Title | ||
|---|---|---|
Application of Phase Congruency for Discriminating Some Lung Diseases Using Chest Radiograph. |
Abstract | ||
|---|---|---|
A novel procedure using phase congruency is proposed for discriminating some lung disease using chest radiograph. Phase congruency provides information about transitions between adjacent pixels. Abrupt changes of phase congruency values between pixels may suggest a possible boundary or another feature that may be used for discrimination. This property of phase congruency may have potential for deciding between disease present and disease absent where the regions of infection on the images have no obvious shape, size, or configuration. Five texture measures calculated from phase congruency and Gabor were shown to be normally distributed. This gave good indicators of discrimination errors in the form of the probability of Type I Error (delta) and the probability of Type II Error (beta). However, since 1 - delta is the true positive fraction (TPF) and beta is the false positive fraction (FPF), an ROC analysis was used to decide on the choice of texture measures. Given that features are normally distributed, for the discrimination between disease present and disease absent, energy, contrast, and homogeneity from phase congruency gave better results compared to those using Gabor. Similarly, for the more difficult problem of discriminating lobar pneumonia and lung cancer, entropy and homogeneity from phase congruency gave better results relative to Gabor. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1155/2015/424970 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Field | DocType | Volume |
Pulmonary medicine,Computer vision,Chest radiograph,Homogeneity (statistics),Computer science,Lung disease,False positive fraction,Pixel,Artificial intelligence,Type I and type II errors,Phase congruency | Journal | 2015 |
ISSN | Citations | PageRank |
1748-670X | 1 | 0.35 |
References | Authors | |
20 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Omar | 1 | 42 | 7.77 |
| Hossein Ebrahimian | 2 | 1 | 0.35 |
| Norliza Mohd. Noor | 3 | 37 | 9.25 |
| Amran Hussin | 4 | 2 | 0.71 |
| Ashari Yunus | 5 | 30 | 3.68 |
| Aziah Ahmad Mahayiddin | 6 | 1 | 0.35 |