G. N. Balaji

Detection of Heart Muscle Damage from Automated Analysis of Echocardiogram Video

ABSTRACT In this work, an approach for heart muscle damage detection from echocardiography sequences is proposed. To exemplify the approach, a system is presented which involves image denoising and enhancement and segmentation of the left ventricle (LV) for extracting the heart wall boundaries. Using the heart wall boundaries global LV parameters are calculated followed by statistical pattern recognition and classification to identify the heart muscle damage or myocardial ischemia (MI). The performance of this algorithm is assessed in 60 real patient data with both normal and abnormal conditions. The experimental results reveal that the proposed method can be used as an effective tool for detection of heart muscle damage or MI automatically.

Detection of Cardiac Abnormality from Measures Calculated from Segmented Left Ventricle in Ultrasound Videos

In this paper a novel and robust automatic LV segmentation by measuring the properties of each connected components in the echocardiogram images and a cardiac abnormality detection method based on ejection fraction is proposed. Starting from echocardiogram videos of normal and abnormal hearts, the left ventricle is first segmented using connected component labeling and from the segmented LV region the proposed algorithm is used to calculate the left ventricle diameter. The diameter derived is used to calculate the various LV parameters. In each heart beat or cardiac cycle, the volumetric fraction of blood pumped out of the left ventricle (LV) and the ejection fraction (EF) were calculated based on which the cardiac abnormality is decided. The proposed method gave an accuracy of 93.3% and it can be used as an effective tool to segment left ventricle boundary and for classifying the heart as either normal or abnormal.

Automatic X-ray Image Classification System

In recent days, computer-aided fracture detection system plays a role in aiding both orthopaedician and a radiologist by providing accurate and fast results. In order to detect the fracture automatically, classification of X-ray images should be automated and it becomes the initial step. Therefore, an attempt has been made and a system is presented in this paper, which involves five image processing steps namely, denoising using high boost filter, enhancement using adaptive histogram equalization, statistical feature extraction, and classification using artificial neural network. To classify the given input X-ray images into the categories head, neck, skull, foot, palm, and spine, the probabilistic neural network, backpropagation neural network, and support vector machine classifiers are employed in classifying X-ray images. The results ascertain an overall accuracy of 92.3% in classifying X-ray images and the presented system can be used as an effective tool for X-ray image classification.

Comparative Analysis of Coherent Routing Using Machine Learning Approach in MANET

Ad hoc network is a network which is dynamic in nature where the mobile nodes form a temporary network in the absence of centralized administration. Due to the absence of centralized administrator in network, routing in mobile ad hoc network (MANET) becomes the fundamental issue which minimizes the selection of an optimal path for routing. Certain performance parameters such as latency, overhead, and packet delivery ratio (PDR) are affected adversely for which numerous techniques are advocated that enhances the selection of efficient and stable path. In the present paper, an attempt is made to select the optimal path and compare the results by varying the number of nodes by using knowledge-based learning algorithm. The optimal path will possess the highest average sum of relay nodes and will be considered as the most optimal and reliable path. We also proposed that analysis of throughput and PDR is better as compared to the traditional methods. The simulation is carried out at NS-2 network simulator, which is employed to implement wired and wireless ad hoc simulation.

Detection and Diagnosis of Dilated and Hypertrophic Cardiomyopathy by Echocardiogram Sequences Analysis

Automating the detection and diagnosis of cardiovascular diseases using echocardiogram sequences is a challenging task because of the presence of speckle noise, less information and movement of chambers. In this paper an attempt has been made to classify the normal hearts, and hearts affected by dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) by automating the segmentation of left ventricle (LV). The segmented LV from the diastolic frames of echocardiogram sequences alone is used for extracting features. The statistical features and Zernike moment features are obtained from extracted diastolic LV and classified using the classifiers namely support vector machine (SVM), back propagation neural network (BPNN) and probabilistic neural network (PNN). An intensive examination over 60 echocardiogram sequences reveals that the proposed method performs well in classifying normal hearts and hearts affected by DCM and HCM. Among the classifiers used the BPNN classifier with the combination of Zernike moment features gave an highest accuracy of 92.08 %.