Name
Abstract | ||
|---|---|---|
It is quite important and difficult for doctors to detect pathologic regions of prostate ultrasonic images. An automated region detection algorithm is proposed to solve this problem, especially for ultrasonic images containing all kinds of noise and speckle. First, all the pixels of an ultrasonic image are fired by Pulse Coupled Neural Network (PCNN). Then after being processed by morphological closing, binary reversing and region labeling, the seeds are detected automatically using PCNN, by which the region of interest (ROI) of the ultrasonic image is detected by Region Growing. In the end, we code the ROI by pseudo-color. Detected pathologic regions can be used for further clinical inspection and quantitative analysis of ultrasonic images. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73814-5_23 | FAW |
Keywords | Field | DocType |
ultrasonic image,detected pathologic region,prostate ultrasonic image,quantitative analysis,clinical inspection,neural network,morphological closing,pathologic region,automated region detection algorithm,pathologic region detection algorithm,image segmentation,region growing,region of interest | Ultrasonic sensor,Computer vision,Closing (morphology),Speckle pattern,Computer science,Algorithm,Image segmentation,Pixel,Region growing,Artificial intelligence,Region of interest,Connected-component labeling | Conference |
Volume | ISSN | ISBN |
4613 | 0302-9743 | 3-540-73813-4 |
Citations | PageRank | References |
1 | 0.40 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Beidou Zhang | 1 | 1 | 0.40 |
| Yide Ma | 2 | 459 | 34.74 |
| Dongmei Lin | 3 | 18 | 3.98 |
| Liwen Zhang | 4 | 25 | 6.25 |