Biomedical subjects

K Haris

Publications and source records attributed to K Haris.

3 recordsLinked to original sources

PubMed2002

Information technology can enhance quality in regional health delivery.

OBJECTIVES: a) The use of information technology (IT) based solutions for quality health delivery in regional health information networks and the study of the enabling factors for their use in a regional health care network from key classes of users such as the medical personnel and the citizens. b) Identification of potential technologies for usage from all citizens and health providers in a regional environment, in all aspects of everyday life. c) Presentation of a generic user model for reference when developing and assessing IT based health delivery solutions. METHODS: After defining the major questions to be addressed, an overview of tele-health and tele-medicine technologies and solutions currently available shall be presented. Further, a generic user model applied to the use of IT based regional health delivery solutions both for the daily life and home care, and for research and clinical routine purposes are presented. Enabling technologies for integration of different IT modules, medical data processing and management procedures and the wireless application protocol (WAP) technology is discussed. RESULTS: Different levels of user applications are presented such as mobile telephony driven health information monitoring and systems integrating electronic health care records with multimedia medical information management and processing modules. CONCLUSIONS: Although IT solutions are advanced and continue to evolve, still the user acceptance and user friendliness issues are unresolved. Mobile telecommunication solutions however may hold the key for wide scale implementation of IT solutions in regional health information networks and increased quality of health services.

Decision Support Systems, Clinical↗

PubMed1999

Model-based morphological segmentation and labeling of coronary angiograms.

A method for extraction and labeling of the coronary arterial tree (CAT) using minimal user supervision in single-view angiograms is proposed. The CAT structural description (skeleton and borders) is produced, along with quantitative information for the artery dimensions and assignment of coded labels, based on a given coronary artery model represented by a graph. The stages of the method are: 1) CAT tracking and detection; 2) artery skeleton and border estimation; 3) feature graph creation; and iv) artery labeling by graph matching. The approximate CAT centerline and borders are extracted by recursive tracking based on circular template analysis. The accurate skeleton and borders of each CAT segment are computed, based on morphological homotopy modification and watershed transform. The approximate centerline and borders are used for constructing the artery segment enclosing area (ASEA), where the defined skeleton and border curves are considered as markers. Using the marked ASEA, an artery gradient image is constructed where all the ASEA pixels (except the skeleton ones) are assigned the gradient magnitude of the original image. The artery gradient image markers are imposed as its unique regional minima by the homotopy modification method, the watershed transform is used for extracting the artery segment borders, and the feature graph is updated. Finally, given the created feature graph and the known model graph, a graph matching algorithm assigns the appropriate labels to the extracted CAT using weighted maximal cliques on the association graph corresponding to the two given graphs. Experimental results using clinical digitized coronary angiograms are presented.

Algorithms↗

PubMed1997

Coronary artery skeleton detection based on topographic features.

A new approach to the problem of detecting the skeletons of coronary arteries in coronary angiograms is proposed. The digitized grayscale coronary angiograms are regarded as noisy sampling of the underlying continuous surface. After the application of Gaussian filtering to reduce noise, the topographic features of the smoothed image are detected based on first and second order image derivatives. Then, the candidate arteries skeleton points are detected based on the observation that arteries are smooth elongated objects having approximately a Gaussian smoothed semi-elliptical profile. Experimental results on real digitized coronary angiograms are presented.

Algorithms↗