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Generally patient data in healthcare environments exist in relational databases. Classification of echocardiographic images is an important data mining task that helps hospitals without transferring the data in any form. This paper proposes a novel method to accomplish this task using naïve Bayesian model via SQL. The proposed method has two phases. The first phase builds a knowledge base using many...
Content Based Image Retrieval (CBIR) is the application of computer vision techniques to retrieve the most visually similar images from the image database for any given query image. The visual characteristics of a disease carry diagnostic information and oftentimes visually similar images correspond to the same disease category. In this paper we aim at building an efficient Content Based Echo Image...
Doppler imaging allows evaluation of blood flow patterns, direction, and velocity. The color (red, blue, and mosaic) signify the direction of the blood flow. By analyzing this color Doppler, it is possible to detect heart diseases like mitral and aortic stenosis, mitral, tricuspid, and aortic regurgitation, and Left Ventricle (LV) hypertrophy. We present 3 methods to extract low level features namely...
Accurate analysis of 2D echocardiographic images is vital for diagnosis and treatment of heart related diseases. For this task, extraction of cardiac borders must be carried out. In particular, automatic quantitative measurements of Left Ventricle (LV), Right Ventricle (RV), Left Atrium (LA), Right Atrium, Valve size, etc. are essential. We believe that automatic processing of these echo images could...
An efficient K-Means clustering algorithm is proposed using the power of SQL in a relational Database Management environment. Further, this method is applied to segment 2D echocardiography images. We propose this method mainly to improve the speed of segmentation process for further clinical analysis and diagnosis (example: Left Ventricular (LV) boundary detection and other 2D quantitative measurements)...
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