| contributor author | K. Mala | |
| contributor author | V. Sadasivam | |
| contributor author | S. Alagappan | |
| date accessioned | 2017-05-09T00:25:18Z | |
| date available | 2017-05-09T00:25:18Z | |
| date copyright | June, 2007 | |
| date issued | 2007 | |
| identifier issn | 1932-6181 | |
| identifier other | JMDOA4-27984#180_1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/136590 | |
| description abstract | The use of ultrasonography as an imaging modality has become widespread because of its ability to visualize the organs with no deleterious effects and low cost. Using ultrasonic liver images focal diseases can be identified by differences in echogenicity between normal and areas affected by diseases. In the presence of diffused disease, however, the entire organ may be affected. In that situation, there is no contrast in echo intensity on which to base a diagnosis. Hence it is difficult for an experienced clinician to diagnose the diffused liver diseases by simple visual interpretations. This can be improved by providing useful information, obtained by computer aided tissue characterization, that cannot be obtained by simple visual interpretation. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Combined Statistical and Multiscale View on Ultrasonic Liver Images for Characterization | |
| type | Journal Paper | |
| journal volume | 1 | |
| journal issue | 2 | |
| journal title | Journal of Medical Devices | |
| identifier doi | 10.1115/1.2735975 | |
| journal fristpage | 180 | |
| journal lastpage | 184 | |
| identifier eissn | 1932-619X | |
| keywords | Liver | |
| keywords | Wavelets | |
| keywords | Artificial neural networks AND Design | |
| tree | Journal of Medical Devices:;2007:;volume( 001 ):;issue: 002 | |
| contenttype | Fulltext | |