Name
Title | ||
|---|---|---|
Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses in Breast DCE-MRI |
Abstract | ||
|---|---|---|
Abstract Our purpose in this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses in dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI). Our database consisted 90 DCE-MRI examinations, each of which contained four sequential phase images; this database included 28 benign masses and 62 malignant masses. In our CAD scheme, we first determined 11 objective features of masses by taking into account the image features and the dynamic changes in signal intensity that experienced radiologists commonly use for describing masses in DCE-MRI. Quadratic discriminant analysis (QDA) was employed to distinguish between benign and malignant masses. As the input of the QDA, a combination of four objective features was determined among the 11 objective features according to a stepwise method. These objective features were as follows: (i) the change in signal intensity from 2 to 5 min; (ii) the change in signal intensity from 0 to 2 min; (iii) the irregularity of the shape; and (iv) the smoothness of the margin. Using this approach, the classification accuracy, sensitivity, and specificity were shown to be 85.6 % (77 of 90), 87.1 % (54 of 62), and 82.1 % (23 of 28), respectively. Furthermore, the positive and negative predictive values were 91.5 % (54 of 59) and 74.2 % (23 of 31), respectively. Our CAD scheme therefore exhibits high classification accuracy and is useful in the differential diagnosis of masses in DCE-MRI images. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10278-015-9856-7 | Journal of Digital Imaging |
Keywords | Field | DocType |
Computer-aided diagnosis,DCE-MRI,Mass | CAD,Signal intensity,Positive predicative value,Computer science,Feature (computer vision),Computer-aided diagnosis,Radiology,Magnetic resonance imaging,Differential diagnosis,Quadratic classifier | Journal |
Volume | Issue | ISSN |
29 | 3 | 1618-727X |
Citations | PageRank | References |
3 | 0.39 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emi Honda | 1 | 3 | 0.39 |
| Ryohei Nakayama | 2 | 14 | 4.80 |
| Hitoshi Koyama | 3 | 3 | 0.39 |
| Akiyoshi Yamashita | 4 | 3 | 0.39 |