N. Chidambaram

Bio: N. Chidambaram is an academic researcher from Annamalai University. The author has contributed to research in topics: Dilated cardiomyopathy & Mitral valve. The author has an hindex of 4, co-authored 6 publications receiving 81 citations.

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.

Cardiac View Classification Using Speed Up Robust Features

Abstract: Objectives: Automating cardiac view classification is the first step for automating computer aided cardiac disease diagnosis. In this paper automatic cardiac view classification system is proposed. Methods: This system attempts to classify four standard cardiac views in echocardiogram namely Parasternal Long Axis (PLAX), Parasternal Short Axis (PSAX), Apical Four Chamber (A4C), and Apical Two Chamber (A2C) views automatically using Speed Up Robust Features (SURF). Conclusion: The Speed Up Robust Features is effective in collecting more class-specific information and ro-bust in dealing with partial occlusion and viewpoint changes. To authenticate the generalizability and robustness, the proposed system is tested on a dataset of 200 echocardiogram images which achieve a classification rate of 90.7%.

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

Abstract: 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 %.

Correction to “Automatic Quality Assessment of Echocardiograms Using Convolutional Neural Networks: Feasibility on the Apical Four-Chamber View”

Abstract: Echocardiography (echo) is a skilled technical procedure that depends on the experience of the operator. The aim of this paper is to reduce user variability in data acquisition by automatically computing a score of echo quality for operator feedback. To do this, a deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber (A4C) echo. In this paper, 6,916 end-systolic echo images were manually studied by an expert cardiologist and were assigned a score between 0 (not acceptable) and 5 (excellent). The images were divided into two independent training-validation and test sets. The network architecture and its parameters were based on the stochastic approach of the particle swarm optimization on the training-validation data. The mean absolute error between the scores from the ultimately trained model and the expert’s manual scores was 0.71 ± 0.58. The reported error was comparable to the measured intra-rater reliability. The learned features of the network were visually interpretable and could be mapped to the anatomy of the heart in the A4C echo, giving confidence in the training result. The computation time for the proposed network architecture, running on a graphics processing unit, was less than 10 ms per frame, sufficient for real-time deployment. The proposed approach has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment. Finally, the approach did not use any specific assumptions about the A4C echo, so it could be generalizable to other standard echo views.

Parametric optimization for floating drum anaerobic bio-digester using Response Surface Methodology and Artificial Neural Network

Abstract: The main purpose of this study to increase the optimal conditions for biogas yield from anaerobic digestion of agricultural waste (Rice Straw) using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). In the development of predictive models temperature, pH, substrate concentration and agitation time are conceived as model variables. The experimental results show that the liner model terms of temperature, substrate concentration and pH, agitation time have significance of interactive effects (p < 0.05). The results manifest that the optimum process parameters affected on biogas yield increase from the ANN model when compared to RSM model. The ANN model indicates that it is much more accurate and reckons the values of maximum biogas yield when compared to RSM model.

A survey of medical image classification techniques

Abstract: Medical informatics is the study that combines two medical data sources: biomedical record and imaging data. Medical image data is formed by pixels that correspond to a part of a physical object and produced by imaging modalities. Exploration of medical image data methods is a challenge in the sense of getting their insight value, analyzing and diagnosing of a specific disease. Image classification plays an important role in computer-aided-diagnosis and is a big challenge on image analysis tasks. This challenge related to the usage of methods and techniques in exploiting image processing result, pattern recognition result and classification methods and subsequently validating the image classification result into medical expert knowledge. The main objective of medical images classification is not only to reach high accuracy but also to identify which parts of human body are infected by the disease. This paper reviewed the state-of-the-art of image classification techniques to diagnose human body disease. The review covered identification of medical image classification techniques, image modalities used, the dataset and trade off for each technique. At the end, the reviews showed the improvement of image classification techniques such as to increase accuracy and sensitivity value and to be feasible employed for computer-aided-diagnosis are a big challenge and an open research.