A Tool for Telediagnosis of Cardiovascular Diseases in a Collaborative and Adaptive Approach

International audienceIn this paper, we present a new telediagnosis environment for the detection of cardiovascular problems. This tool, called VACODIS (VAscular COllaborative teleDIagnosiS), allows practitioners to semi-automatically identify and quantify a patient's potential cardiovascular complications. The system generates first-time automatic detection of cardiovascular abnormalities using Doppler ultrasound images. The system then provides remote collaborative sharing of this information among different doctors to allow distance telediagnostics. With this new system, different actors in the field of medicine (nurses, practitioners, etc.) will be able to contribute to a more reliable diagnosis in the cardiovascular domain

Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clustering from Ultrasound Images

Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain

Developing an automatic brachial artery segmentation and bloodstream analysis tool using possibilistic C-means clustering from color doppler ultrasound images

Automatic segmentation of brachial artery and blood-flow dynamics are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose a software that is noise tolerant and fully automatic in segmentation of brachial artery from color Doppler ultrasound images. Possibilistic C-Means clustering algorithm is applied to make the automatic segmentation. We use HSV color model to enhance the contrast of bloodstream area in the input image. Our software also provides index of hemoglobin distribution with respect to the blood flow velocity for pathologists to proceed further analysis. In experiment, the proposed method successfully extracts the target area in 59 out of 60 cases (98.3%) with field expert’s verification

Detection and diagnosis of dilated cardiomyopathy and hypertrophic cardiomyopathy using image processing techniques

AbstractMajor heart diseases like heart muscle damage and valvular problems are diagnosed using echocardiogram. Since the echocardiogram is an image or sequence of images with less information the cardiologist spends more time to predict or to make decision. Automating the detection and diagnosis of dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) is a key enabling technology in computer aided diagnosis systems. In this paper, a system is proposed to automatically detect and diagnose dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). This system performs denoising, enhancement, before left ventricular segmentation is carried out in the individual frames. Using the segmented left ventricle, the LV parameters like volume and ejection fraction (EF) are calculated and also the end-diastolic LV is extracted. The PCA and DCT features are obtained from the extracted end-diastolic LV and the classifiers BPNN, SVM and combined K-NN are used to classify the normal hearts, hearts affected with DCM and hearts affected with HCM. The PCA feature with BPNN classifier gives a highest overall accuracy of 92.04% in classifying normal and abnormal hearts. Experiments over 60 echocardiogram videos expose that the proposed system can be effectively utilized to detect and diagnose DCM and HCM