SUN MIN PARK, N.P.
Nursing at Childrens Way, San Diego, CA
License number
California NP20538
Category
Nursing
Type
Nurse Practitioner
Address
Address
3030 Childrens Way STE 410, San Diego, CA 92123
Phone
(858) 966-6789
(858) 309-6300
(858) 309-6291 (Fax)
(858) 309-6300
(858) 309-6291 (Fax)
Personal information
See more information about SUN MIN PARK at radaris.comName
Address
Phone
417 Santa Maria, Irvine, CA 92606
417 S Mariposa Ave APT 307, Los Angeles, CA 90020
417 N Ardmore Ave APT 6, Los Angeles, CA 90004
4001 W Garden Grove Blvd APT 406, Orange, CA 92868
405 S Wilton Pl, Los Angeles, CA 90020
Professional information
See more information about SUN MIN PARK at trustoria.com
Sun Min Park, San Diego CA - NP (Nurse practitioner)
Sun Min Park, San Diego CA - NP (Nurse practitioner)
Specialties:
Nursing (Nurse Practitioner)
Address:
3030 Childrens Way SUITE 410, San Diego 92123
(858) 966-6789 (Phone)
(858) 966-6789 (Phone)
Languages:
English
Hospitals:
3030 Childrens Way SUITE 410, San Diego 92123
The Johns Hopkins Hospital
1800 Orleans St, Baltimore 21287
The Johns Hopkins Hospital
1800 Orleans St, Baltimore 21287
Sun Min Park, San Diego CA
Sun Min Park, San Diego CA
Specialties:
Nurse Practitioner
Address:
3030 Childrens Way, San Diego, CA 92123
3860 Calle Fortunada, San Diego, CA 92123
3860 Calle Fortunada, San Diego, CA 92123
Image Analysis For Cervical Neoplasia Detection And Diagnosis
Image Analysis For Cervical Neoplasia Detection And Diagnosis
US Patent:
8503747, Aug 6, 2013
Filed:
May 3, 2011
Appl. No.:
13/068188
Inventors:
Sun Young Park - San Diego CA, US
Dustin Sargent - San Diego CA, US
Ulf Peter Gustafsson - San Diego CA, US
Dustin Sargent - San Diego CA, US
Ulf Peter Gustafsson - San Diego CA, US
Assignee:
STI Medical Systems, LLC - La Jolla CA
International Classification:
G06K 9/00, A61B 6/00
US Classification:
382128, 382224, 378 18
Abstract:
The present invention is an automated image analysis framework for cervical cancerous lesion detection. The present invention uses domain-specific diagnostic features in a probabilistic manner using conditional random fields. In addition, the present invention discloses a novel window-based performance assessment scheme for two-dimensional image analysis, which addresses the intrinsic problem of image misalignment. As a domain-specific anatomical feature, image regions corresponding to different tissue types are extracted from cervical images taken before and after the application of acetic acid during a clinical exam. The unique optical properties of each tissue type and the diagnostic relationships between neighboring regions are incorporated in the conditional random field model. The output provides information about both the tissue severity and the location of cancerous tissue in an image.
Diagnosis Support System Providing Guidance To A User By Automated Retrieval Of Similar Cancer Images With User Feedback
Diagnosis Support System Providing Guidance To A User By Automated Retrieval Of Similar Cancer Images With User Feedback
US Patent:
2012028, Nov 8, 2012
Filed:
May 4, 2012
Appl. No.:
13/464914
Inventors:
Sun Young Park - San Diego CA, US
Dustin Michael Sargent - San Diego CA, US
Rolf Holger Wolters - Honolulu HI, US
Ulf Peter Gustafsson - San Diego CA, US
Dustin Michael Sargent - San Diego CA, US
Rolf Holger Wolters - Honolulu HI, US
Ulf Peter Gustafsson - San Diego CA, US
International Classification:
A61B 1/303, A61B 6/00
US Classification:
600476
Abstract:
The present invention is a diagnosis support system providing automated guidance to a user by automated retrieval of similar disease images and user feedback. High resolution standardized labeled and unlabeled, annotated and non-annotated images of diseased tissue in a database are clustered, preferably with expert feedback. An image retrieval application automatically computes image signatures for a query image and a representative image from each cluster, by segmenting the images into regions and extracting image features in the regions to produce feature vectors, and then comparing the feature vectors using a similarity measure. Preferably the features of the image signatures are extended beyond shape, color and texture of regions, by features specific to the disease. Optionally, the most discriminative features are used in creating the image signatures. A list of the most similar images is returned in response to a query. Keyword query is also supported.