ABSTRACT
In this work, an algorithm for the detection of the left ventricular border in two-dimensional long axis echocardiographic images is presented. In its preprocessing stage, images fusion was applied to a sequence of images composed of three cardiac cycles. This method exploits the similarity of corresponding frames from different cycles and produces contrast enhancement in the left ventricular boundary. This result improves the performance of the segmentation stage which is based on watershed transformation. The obtained left ventricle border is quantitatively and qualitatively compared with contours manually segmented by a cardiologist, and with results obtained using seven different techniques from the literature.
Subject(s)
Algorithms , Cardiac-Gated Imaging Techniques/methods , Echocardiography/methods , Image Enhancement/methods , Image Interpretation, Computer-Assisted/methods , Subtraction Technique , Humans , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
This work presents a new approach for classification and retrieval of echocardiographic images from textual information of the anatomical structures and diagnosis features. These textual attributes will be acquired from the electronic medical report generated in an integrated healthcare environment. The medical report is provided by a specialist in the area during the analysis of the medical image stored in a PACS environment. Such innovation guarantees a more accurate classifier and a better optimization of the medical work, since the medical report and the attributes for the medical image classifier will be created at the same time. The system is being developed in the University Hospital of the University of Brasilia.
