Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Breast cancer is the utmost frequent cancer amid women. The efficiency of cancer treatments on tumor development is carried out using a most sensitive method dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Neoadjuvant chemotherapy (NAC) treatment is widely used in patients for early prediction of breast cancer. The development of extraction of high throughput of features is referred to radiomics. The purpose of this study is to determine the capability of radiomic features in the breast region respond to the treatment. Radiomic analysis was performed on 20 studies of 10 patients using DCE-MRI. A total of 94 three-dimensional radiomic features were extracted and examined statistically. Results explain that the five features 90 and 99 percentile of histogram intensity, mean 3D, variance 3D, and short-run emphasis are significant with p-value from 0.0025 to 0.0694. Hence, these radiomic features are potency to identify the ambivert changes in the breast region during the follow-ups. © 2021, Springer Nature Singapore Pte Ltd.

World University root node

Department of Sensor and Biomedical Technology

School of Electronics Engineering

Vellore Campus

Lecture Notes in Electrical Engineering

Radiomics Based Analysis of Breast Tumors in DCE-MRI due to Neoadjuvant Treatment Therapy

The basic vital parameters of the Human body can be detected from Plethysmography. The PPG signal helps in determining the heart rate of the person, along with SPo2,Blood Pressure and Blood Glucose. Through this monitoring system the doctors could identify the health status of the patient. In this paper, we have used Cloud computing technology in order to monitor the patient and retrieve the data by Alexa which has been connected to it. Considering the cloud computing technology, we have used AWS cloud for the storage of the PPG signal and also for running the algorithm inside the cloud. The smart watch (wearable) is used to collect the PPG data. This could be passed over the cloud for determining the heart rate and the parameter are retrieved with the help of Alexa. Here the Alexa also involved in giving the alert in case of abnormal ranges. © 2020 IEEE.

Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020

Detection of Vital Parameters using Cloud Computing Technology

Composites Part B: Engineering

Classification of damages in composite images using Zernike moments and support vector machines

Nadi Pariksha is a non-invasive pulse diagnosis method for assessing health of person in Ayurveda. The disease caused can be related to imbalance in any of three doshas i.e. vatha, pittha and kapha. The health status of the person is examined by Ayurvedic physician by feeling palpation from three fingers (index, middle and ring) placed on the radial artery for vatha, pittha and kapha respectively. In present research work the design and development of the pulse diagnosis system was carried out, in which Photoplethysmography (PPG) sensor was interfaced with Arduino was used to acquire wrist pulse signals from subjects in the form of data sets. Matlab software was used in signal processing after performing frequency domain analysis using power spectrum to design a filter and feature extraction was performed on these signals. Algorithms for three parameters were designed related to pulse sensors namely pulse wave velocity, augmentation index and reflectivity index. Data sets were created with the help of hundred volunteers. After performing statistical analysis on these data sets a standard signal was established. Finally this experimental setup was used to diagnose patients with symptoms associated with imbalance in Tridosha to look for deviations from the standard vatha pittha kapha signal and these results were compared with theoretical results as per Ayurveda. Based on this research we came up with a conclusion with respect to the system designed. © 2019 IEEE.

VIT Unitversity

2019 Innovations in Power and Advanced Computing Technologies (i-PACT)

Pulse diagnosis System for Nadi Pariksha using parametric and statistical analysis

Breast cancer disorders are leading cause of morbidity and mortality worldwide. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is the most common method of assessing the response to chemotherapy in breast cancer treatment monitoring. Radiomic features obtained from MR images have potential in reflecting the tumor biology. In this work, an attempt has been made to investigate the clinical potential of breast DCE-MRI derived radiomic features and its response to Neoadjuvant Chemotherapy (NAC). The data used in this study (10 Patients with 20 studies (Visit-1 Visit-2) were obtained from public domain Quantitative Imaging Network (QIN) Breast DCE-MRI database. Using Mazda software, the radiomic features were extracted from whole breast region to quantify the pathological variations during visit-1 and visit-2. Totally, 176 texture and shape features were extracted and analyzed statistically using student's t test. Result shows that, the radiomic features were able to differentiate the variations in the tumor biology during visit-1 and visit-2 due to NAC. The features such as GeoW2, GeoW3, GeoW4, GeoRs, GeoRc, GeoRm, 50 percentile of histogram intensity and Theta1 were found to be statistically significant with p values ranging from 0.03 to 0.08. Hence it appears that, the radiomic features could be used as adjunct measure in reflecting the pathological response during NAC and thus this study seems to be clinically significant. © 2019 IEEE.

IST 2019 - IEEE International Conference on Imaging Systems and Techniques, Proceedings

Extraction of radiomic features from breast dce-mri responds to pathological changes in patients during neoadjuvant chemotherapy treatment

2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS)

Design of IoT based generic health care system

Metacarpus flexion and palmar prehension using wrist movement based MEMS accelerometer

The development of reliable Computer Aided Diagnosis (CAD) systems would help in the early detection of Knee Joint Disorder (KJD). In this work, normal and KJD vibroarthrographic (VAG) signals are classified using multifractals and Support Vector Machines (SVM). Multifractal dimension [Formula: see text] is calculated from the VAG signals for various [Formula: see text]-values ([Formula: see text]). Geometrical features are calculated from the multifractal spectrum. The dimension of the feature set is reduced using Principal Component Analysis (PCA). The significant features obtained from the multifractal spectrum are fed as the input to the SVM classifier. The accuracy of the classifier is analyzed using kernels such as linear, quadratic, polynomial and Radial Basis Functions (RBF). The results suggest that VAG signals exhibits the multifractal property. The fluctuations in the normal and abnormal signals are well predicted in small scales of segments of time series. The features such as [Formula: see text] and Mean[Formula: see text] are high in abnormal VAG signals. These features give statistically significant values in differentiating the normal and abnormal subjects ([Formula: see text]). The area under the Receiver Operating Characteristic (ROC) curve is high for polynomial function (0.98). The SVM classifier with polynomial function gives 92.13% of accuracy in differentiating the normal and abnormal subjects. The calculation of multifractal spectrum and geometrical features from VAG signals requires optimization of few parameters, easy to compute, computationally inexpensive, and less time consuming. Hence, the CAD system seems to be clinically significant for the classification of normal and KJD subjects.

Biomedical Engineering: Applications, Basis and Communications

CLASSIFICATION OF NORMAL AND KNEE JOINT DISORDER VIBROARTHROGRAPHIC SIGNALS USING MULTIFRACTALS AND SUPPORT VECTOR MACHINES

Spirometry is a valuable tool used for respiratory diagnoses and assessment of disease progression. It measures air flow to help make a definitive diagnosis of pulmonary disorder and confirms presence of airway obstruction. In this work, clustering based classification of spirometric pulmonary function data has been attempted using Principal Component Analysis (PCA) and Self Organising Map (SOM). Pulmonary function data (N=100) are obtained from normal and obstructive subjects using gold standard Spirolab II spirometer. These data are subjected to PCA to extract significant parameters relevant to the cluster structure. The clustering analysis of the significant spirometric parameters is further enhanced using self organizing map and classification of spirometric data is achieved. It is observed from results that FEV1, PEF and FEF25-75% are found to be significant in differentiating normal and obstructive subjects. SOM based classification is able to achieve accuracy of 95%. This cluster based method of feature reduction and classification could be useful in assessing the pulmonary function disorders for spirometric pulmonary function test with large dataset. © 2013 Springer International Publishing.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Clustering based analysis of spirometric data using principal component analysis and self organizing map

Spirometric evaluation of pulmonary function plays a critical role in the diagnosis, differentiation and management of respiratory disorders. In spirometry, there is a requirement that a large database is to be analyzed by the physician for effective investigation. Hence, there is a need for automated evaluation of spirometric parameters to diagnose respiratory abnormalities in order to ease the work of the physician. In this work, a neuro fuzzy based Adaptive Neuro Fuzzy Inference System (ANFIS), Multiple ANFIS and Complex valued ANFIS models are employed in classifying the spirometric data. Four different membership functions which include triangular, trapezoidal, Gaussian and Gbell are employed in these classification models. Results show that all the models are capable of classifying respiratory abnormalities. Also, it is observed that CANFIS model with Gaussian membership function performs better than other models and achieved higher accuracy. This study seems to be clinically relevant as this could be useful for mass screening of respiratory diseases. © 2012 Springer-Verlag.

Classification of respiratory abnormalities using adaptive neuro fuzzy inference system

In this work, the primary and secondary tensile strength components of human femur trabecular bone are qualitatively assessed using planar radiographic images and spectral analysis. Normal and abnormal femur trabecular images (N = 40) were recorded using planar radiography following standard image acquisition protocol. From the images, the tensile strength components of the trabeculae are delineated using image processing procedures and are then subjected to Fourier transform. The zero (DC), First (FMOI), and Second Moments of Inertia (SMOI) are the parameters considered and are correlated with presence and absence of mineralization in the image. Results show that the values of moments correlate well with percentage mineralization in normal images when compared to abnormal images for both primary and secondary tensile strength components. Further, no or poor correlations were found for abnormals in all cases. Among all, the values of second moment showed highest correlation in the secondary tensile region. In this paper the objectives, methodology, significance results and the conclusions are presented. © 2009 World Scientific Publishing Company.

Journal of Mechanics in Medicine and Biology

Qualitative assessment of tensile strength components of human femur trabecular bone using radiographic imaging and spectral analysis

Odonata population of Annamalainagar, Tamil Nadu was surveyed and 23 species under four families and 21 genera were collected and identified. Suborder Zygoptera is represented by the family Coenagrionidae and Anisoptera by the families Libellulidae, Aeshnidae and Gomphidae. Various genera and species collected are reported with their distribution in India.