| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_819d2d00412d671253120d16a2f09908 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30041
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30101
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10101 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-155
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-006
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-23213
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-187
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-28
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-38
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-187
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-155
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-30 |
| filingDate |
2019-07-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_79fadd94a387020d163ce804e771a870
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5e070ce3f30139dda7b5f567f5e3c3b5
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c8843de66f926a5d4eac6cb20462b0ab |
| publicationDate |
2019-11-05-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-110415216-A |
| titleOfInvention |
An automatic detection method of CNV based on SD-OCT and OCTA retinal images |
| abstract |
The invention discloses an automatic detection method for choroidal neovascularization (CNV) based on frequency domain optical coherence tomography (SD-OCT) and optical coherence tomography angiography (OCTA) images, belonging to the technical field of retinal image processing. The method includes the following contents: collecting SD‑OCT retinal images and OCTA retinal images containing CNV lesions; performing layer segmentation on the retinal images, and projecting the 3D volume data to obtain a 2D projection map; Binarize the projection map of , and merge the target candidate regions in the dual modalities; remove the false target candidate regions according to the number of contained seed points to obtain a rough CNV region; cluster the pixels inside the rough CNV region to refine the boundary. Compared with the traditional single-modal image-based detection method, the present invention has higher detection accuracy and robustness. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115409689-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114271791-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112057049-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115409689-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114271791-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112057049-A |
| priorityDate |
2019-07-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |