Name
Title | ||
|---|---|---|
Computerized determination scheme for histological classification of breast mass using objective features corresponding to clinicians' subjective impressions on ultrasonographic images. |
Abstract | ||
|---|---|---|
It is often difficult for clinicians to decide correctly on either biopsy or follow-up for breast lesions with masses on ultrasonographic images. The purpose of this study was to develop a computerized determination scheme for histological classification of breast mass by using objective features corresponding to clinicians' subjective impressions for image features on ultrasonographic images. Our database consisted of 363 breast ultrasonographic images obtained from 363 patients. It included 150 malignant (103 invasive and 47 noninvasive carcinomas) and 213 benign masses (87 cysts and 126 fibroadenomas). We divided our database into 65 images (28 malignant and 37 benign masses) for training set and 298 images (122 malignant and 176 benign masses) for test set. An observer study was first conducted to obtain clinicians' subjective impression for nine image features on mass. In the proposed method, location and area of the mass were determined by an experienced clinician. We defined some feature extraction methods for each of nine image features. For each image feature, we selected the feature extraction method with the highest correlation coefficient between the objective features and the average clinicians' subjective impressions. We employed multiple discriminant analysis with the nine objective features for determining histological classification of mass. The classification accuracies of the proposed method were 88.4 % (76/86) for invasive carcinomas, 80.6 % (29/36) for noninvasive carcinomas, 86.0 % (92/107) for fibroadenomas, and 84.1 % (58/69) for cysts, respectively. The proposed method would be useful in the differential diagnosis of breast masses on ultrasonographic images as diagnosis aid. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10278-013-9594-7 | J. Digital Imaging |
Keywords | Field | DocType |
Computer-aided diagnosis,Histological classification,Observer study,Feature extraction method,Ultrasonographic image | Training set,Computer vision,Feature (computer vision),Multiple discriminant analysis,Computer-aided diagnosis,Biopsy,Feature extraction,Artificial intelligence,Radiology,Linear discriminant analysis,Medicine,Differential diagnosis | Journal |
Volume | Issue | ISSN |
26 | 5 | 1618-727X |
Citations | PageRank | References |
1 | 0.34 | 8 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akiyoshi Hizukuri | 1 | 5 | 2.16 |
| Ryohei Nakayama | 2 | 14 | 4.80 |
| Yumi Kashikura | 3 | 1 | 0.34 |
| Haruhiko Takase | 4 | 38 | 17.24 |
| Hiroharu Kawanaka | 5 | 37 | 23.25 |
| Tomoko Ogawa | 6 | 1 | 0.68 |
| Shinji Tsuruoka | 7 | 186 | 35.56 |