9000 Rockville Pike Suite 10, Bethesda, MD 20892 (301)4025486 (Phone)
1500 E Medical Center Dr, Ann Arbor, MI 48109
Certifications:
Diagnostic Radiology, 1993
Awards:
Healthgrades Honor Roll
Languages:
English
Education:
Medical School Perelman School of Medicine University of Pennsylvania Graduated: 1988 Medical School Presby Med Ctr Graduated: 1988 Medical School University Of Michigan Hospitals Graduated: 1988 Medical School Duke University Hospital Graduated: 1988
Wake Orthopaedics LLC 8001 T W Alexander Dr STE 224, Raleigh, NC 27617 (919)7147152 (phone), (919)2325021 (fax)
Education:
Medical School University of Kansas School of Medicine Graduated: 1992
Procedures:
Hip/Femur Fractures and Dislocations Arthrocentesis Carpal Tunnel Decompression Hallux Valgus Repair Hip Replacement Joint Arthroscopy Knee Arthroscopy Knee Replacement Lower Arm/Elbow/Wrist Fractures and Dislocations Lower Leg/Ankle Fractures and Dislocations Occupational Therapy Evaluation Shoulder Arthroscopy Shoulder Surgery
Conditions:
Internal Derangement of Knee Intervertebral Disc Degeneration Rotator Cuff Syndrome and Allied Disorders Fractures, Dislocations, Derangement, and Sprains Internal Derangement of Knee Cartilage
Languages:
English Spanish
Description:
Dr. Summers graduated from the University of Kansas School of Medicine in 1992. He works in Raleigh, NC and 2 other locations and specializes in Orthopaedic Surgery and Orthopedic Sports Medicine. Dr. Summers is affiliated with Wakemed Cary Hospital and Wakemed Raleigh Campus.
Us Patents
Method For Segmenting Medical Images And Detecting Surface Anomalies In Anatomical Structures
Ronald M. Summers - Potomac MD Scott Selbie - Rockville MD James D. Malley - Rockville MD Lynne M. Pusanik - Columbia MD
Assignee:
The United States of America as represented by the Department of Health and Human Services - Washington DC
International Classification:
G06K 900
US Classification:
382128
Abstract:
A region growing method segments three-dimensional image data of an anatomical structure using a tortuous path length limit to constrain voxel growth. The path length limit constrains the number of successive generations of voxel growth from a seed point to prevent leakage of voxels outside the boundary of the anatomical structure. Once segmented, a process for detecting surface anomalies performs a curvature analysis on a computer model of the surface of the structure. This process detects surface anomalies automatically by traversing the vertices in the surface model, computing partial derivatives of the surface at the vertices, and computing curvature characteristics from the partial derivatives. To identify possible anomalies, the process compares the curvature characteristics with predetermined curvature characteristics of anomalies and classifies the vertices. The process further refines potential anomalies by segmenting neighboring vertices that are classified as being part of an anomaly using curvature characteristics.
Method For Segmenting Medical Images And Detecting Surface Anomalies In Anatomical Structures
Ronald M. Summers - Potomac MD Scott Selbie - Rockville MD James D. Malley - Rockville MD Lynne M. Pusanik - Columbia MD
Assignee:
The United States of America as represented by the Department of Health and Human Services - Washington DC
International Classification:
G06K 900
US Classification:
382128
Abstract:
A region growing method segments three-dimensional image data of an anatomical structure using a tortuous path length limit to constrain voxel growth. The path length limit constrains the number of successive generations of voxel growth from a seed point to prevent leakage of voxels outside the boundary of the anatomical structure. Once segmented, a process for detecting surface anomalies performs a curvature analysis on a computer model of the surface of the structure. This process detects surface anomalies automatically by traversing the vertices in the surface model, computing partial derivatives of the surface at the vertices, and computing curvature characteristics from the partial derivatives. To identify possible anomalies, the process compares the curvature characteristics with predetermined curvature characteristics of anomalies and classifies the vertices. The process further refines potential anomalies by segmenting neighboring vertices that are classified as being part of an anomaly using curvature characteristics.
Computer-Aided Classification Of Anomalies In Anatomical Structures
Ronald M. Summers - Potomac MD, US Marek Franaszek - Gaithersburg MD, US Gheorghe Iordanescu - Rockville MD, US
Assignee:
The United States of America as represented by the Secretary of the Department of Health and Human Services - Washington DC
International Classification:
G06K 9/00
US Classification:
382128, 382286
Abstract:
Candidate anomalies in an anatomical structure are processed for classification. For example, false positives can be reduced by techniques related to the anomaly's neck, wall thickness associated with the anomaly, template matching performed for the anomaly, or some combination thereof. The various techniques can be combined for use in a classifier, which can determine whether the anomaly is of interest. For example, a computed tomography scan of a colon can be analyzed to determine whether a candidate anomaly is a polyp. The technologies can be applied to a variety of other scenarios involving other anatomical structures.
Ronald Summers - Potomac MD, US Jianhua Yao - Columbia MD, US C. Daniel Johnson - Rochester MD, US
Assignee:
The United States of America as represented by the Department of Health and Human Services - Washington DC Mayo Foundation for Medical Education and Research - Rochester MN
International Classification:
G06K 9/00
US Classification:
382128, 382131, 382132
Abstract:
Portions of a virtual colon can be analyzed to identify a normal structure, such as an ileocecal valve. Paradigmatic characteristics of ileocecal valves can be used to identify a digital representation as an ileocecal valve. Upon determination that a digital representation has the characteristics of an ileocecal valve, action can be taken. For example, the digital representation can be removed from a list of polyp candidates.
Determination Of Feature Boundaries In A Digital Representation Of An Anatomical Structure
Jianhua Yao - Laurel MD, US Ronald M. Summers - Potomac MD, US
Assignee:
The United States of America as represented by the Department of Health and Human Services - Washington DC
International Classification:
G06K 9/00
US Classification:
382128, 382130, 382131, 382132, 382154, 382224
Abstract:
A virtual anatomical structure can be analyzed to determine enclosing three-dimensional boundaries of features therein. Various techniques can be used to determine tissue types in the virtual anatomical structure. For example, tissue types can be determined via an iso-boundary between lumen and air in the virtual anatomical structure and a fuzzy clustering approach. Based on the tissue type determination, a deformable model approach can be used to determine an enclosing three-dimensional boundary of a feature in the virtual anatomical structure. The enclosing three-dimensional boundary can be used to determine characteristics of the feature and classify it as of interest or not of interest.
Automated Centerline Detection Algorithm For Colon-Like 3D Surfaces
Gheorghe Iordanescu - Rockville MD, US Ronald M. Summers - Potomac MD, US Juan Raul Cebral - Vienna VA, US
Assignee:
The United States of America as represented by the Secretary of the Department of Health and Human Services - Washington DC
International Classification:
G06K 9/00
US Classification:
382154, 382128, 382130, 382131, 382133, 600407
Abstract:
A three dimensional image of the colon like surface is processed to determine at least its ring structure. The image is composed of vertex points, each vertex point having a discrete point identifier and three dimensional position information. The three dimensional position information is averaged in a shrinking procedure to contract the three dimensional image proximate to a major axis of the colon-like surface. Evenly spaced points are taken through the shrunken colon like surface and connected to form a curve. Planes are generated at intervals normal to the curve to split the shrunken colon like surface into image segments. By mapping these image segments back to the original image through their discrete point identifiers, an accurate ring profile of the colon like surface can be generated.
Teniae Coli Guided Navigation And Registration For Virtual Colonoscopy
Hui-Yang Huang - Rockville MD, US Dave A. Roy - Dallas TX, US Ronald M. Summers - Potomac MD, US
Assignee:
The United States of America as represented by the Secretary of Health and Human Services - Washington DC
International Classification:
A61B 5/05
US Classification:
600425, 600407
Abstract:
A computer-assisted method for detecting surface features in a virtual colonoscopy. The method includes providing a three-dimensional construction of a computed tomography colonography surface; creating a path along the teniae coli from the proximal ascending colon to the distal descending colon on the colonography surface; forming an indexed computed tomography colonography surface using the created path; and registering the supine and prone scans of the computed tomography colonography surface using the indexed computed tomography colonography surface. The method also includes navigating the internal surface of the computed tomography colonography using the indexed computed tomography colonography surface.
Computer-Aided Classification Of Anomalies In Anatomical Structures
Ronald M. Summers - Potomac MD, US Marek Franaszek - Gaithersburg MD, US Gheorghe Iordanescu - Rockville MD, US
Assignee:
The United States of America as represented by the Department of Health and Human Services - Washington DC
International Classification:
G06K 9/00
US Classification:
382128
Abstract:
Candidate anomalies in an anatomical structure are processed for classification. For example, false positives can be reduced by techniques related to the anomaly's neck, wall thickness associated with the anomaly, template matching performed for the anomaly, or some combination thereof. The various techniques can be combined for use in a classifier, which can determine whether the anomaly is of interest. For example, a computed tomography scan of a colon can be analyzed to determine whether a candidate anomaly is a polyp. The technologies can be applied to a variety of other scenarios involving other anatomical structures.
Name / Title
Company / Classification
Phones & Addresses
Ronald E. Summers
RON SUMMERS ROOFING, LLC
Ronald E Summers Vice President
COLUMBIA RIVER SAND & GRAVEL, INC
Isbn (Books And Publications)
ITAB 2003: Conference Proceedings, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 24-26 April 2003, Birmingham, United Kingdom New Solutio