Name
Title | ||
|---|---|---|
Computerized classification method for histological classifications of masses using objective features based on clinicians' subjective impressions |
Abstract | ||
|---|---|---|
Our purpose of this study is to develop a computerized classification method for histological classifications of masses using objective features based on clinicians' subjective impressions. An observer study is first conducted to obtain clinicians' subjective impression for nine image features on mass. Nine image features are selected by taking into account image features that clinicians' commonly used for describing masses on ultrasonographic images. In the proposed method, the location and the area of mass are determined by an experienced clinician. We define some extraction methods for each of nine image features. The extraction method is selected such that the correlation coefficient would become the highest between objective features and average of clinicians' subjective ratings. An artificial neural network (ANN) with the nine objective features is employed for distinguishing among four different types of histological classifications on masses. The classification accuracies of the proposed method were evaluated by using 216 ultrasonographic images. The classification accuracies of the proposed method were 85.9% (55/64) for invasive carcinomas, 83.3% (30/36) for noninvasive carcinomas, 89.1% (49/55) for cysts, and 82.0% (50/61) fibroadenomas, respectively. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/SCIS-ISIS.2012.6505028 | SCIS&ISIS |
Keywords | Field | DocType |
biological tissues,biomedical ultrasonics,feature extraction,image classification,medical image processing,neural nets,ann,artificial neural network,clinician subjective impressions,clinician subjective ratings,computerized classification method,correlation coefficient,cysts,fibroadenomas,histological mass classification,image features,noninvasive carcinomas,objective feature extraction method,ultrasonographic images,computer-aided diagnosis,feature extraction method,histological classification,observer study,ultrasonographic image | Correlation coefficient,Feature (computer vision),Computer science,Artificial intelligence,Artificial neural network,Observer (quantum physics),Machine learning | Conference |
ISSN | ISBN | Citations |
2377-6870 | 978-1-4673-2742-8 | 0 |
PageRank | References | Authors |
0.34 | 1 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akiyoshi Hizukuri | 1 | 5 | 2.16 |
| Ryohei Nakayama | 2 | 14 | 4.80 |
| yumi kashikura | 3 | 0 | 0.34 |
| Hiroharu Kawanaka | 4 | 37 | 23.25 |
| Haruhiko Takase | 5 | 38 | 17.24 |
| Tomoko Ogawa | 6 | 1 | 0.68 |
| Shinji Tsuruoka | 7 | 186 | 35.56 |