1. Biomedical Signal Processing, Medical Image Analysis, Pattern Recognition, Machine Learning for clinical aid

1. Signal Processing, Image Processing, Medical Image Analysis, Early diagnosis, Non-invasive disease prognosis

Research

1. Predoctoral Fellow at Indian Institute of Technology Madras

1. Rohini P, Sundar S and Ramakrishnan S (2020), Differentiation of Early mild cognitive impairment in MR images using multifractal detrended moving average singularity spectral features, Biomedical Signal Processing and Control, 57, e101780.

2. Rohini P, Sundar S, Ramakrishnan S, (2019) “Differentiation of EMCI in sMR images using segmented brainstem multifractal texture measures”, Electronic letters, 55(23), 1213 – 1214.

3. Rohini P, Sundar S, Ramakrishnan S, (2019) “Characterization of Alzheimer conditions in MR images using volumetric and sagittal brainstem texture features”, Computer Methods and Programs in Biomedicine, 173, 147-155.

4. Dadsena R, Rohini P, Anandh K R, Ramakrishnan S, (2019) “Comparison of Legendre and united moments in the classification of Alzheimer conditions using T1 weighted MR images”, Electronics Letters, 55(8), 440-442.

5. Rohini P, Sundar S, Ramakrishnan S, (2018) “Assessment of Alzheimer Disease Progression using Texture Analysis in Magnetic Resonance Images”, Biomedical Sciences Instrumentation, 54 (1), 236-243

6. Ravi Dadsena, Rohini P, Anandh K R, Ramakrishnan S (2018) “An Approach to Differentiate Alzheimer Conditions using MR image based Zernike and Fractal Features”, IEIE Transactions on Smart Processing & Computing, 7(3), 175-183.

1. Rohini P and Ramakrishnan S, (2020) “Comparison of monofractal and multifractal features using machine learning performance metrics in classifying Early Mild Cognitive Impairment”, SIIM Annual meeting , Austin.

2. Rohini P and Ramakrishnan S, (2020) “Identification of EMCI in MR Brainstem structure using Fractal measures and Random forest approach”, 30th Medical Informatics Europe Conference (MIE), Geneva.

3. Rohini P and Ramakrishnan S, (2019) “A method to extract MR image biomarkers for non-age related neurodegenerative changes”, Diagnostics-4-Future Conference, Konstanz.

4. Rohini P , Sundar S and Ramakrishnan S, (2019) “Brainstem Median Ternary Pattern analysis in Early Mild Cognitive Impairment using Radial basis function based support vector machine”, The Big Brain 2019, Moscow.

5. Dadsena R, Rohini P , Ramakrishnan S, (2019) “Proposal of a Machine Learning Approach to Differentiate Mild and Alzheimer's Condition in MR Images Using Shape Changes in Corpus Callosum”, EFMI-STC (pp. 243-244).

6. Rohini P, Sundar S, Ramakrishnan S, (2018) “An approach to differentiate Brainstem structures in Alzheimer MR Images using Gaussian Mixture Model and Higuchi Fractals”, Pattern Recognition in Neuroimaging, Singapore.

7. Rohini P, Sundar S, Ramakrishnan S, (2018) “Alzheimer’s Brainstem Assessment in MR images using Texture Energy metrics and Multilayer Perceptron”, SIIM Annual Meeting, Maryland.

8. Ravi Dadsena, Rohini P, Ramakrishnan S, (2018) “An Approach to Differentiate Normal and Mild Cognitive Impairment using machine learning algorithms”, SIIM Annual Meeting, Maryland.

9. Ravi Dadsena, Rohini P, Ramakrishnan S, (2018) “An Approach to Differentiate Normal and Mild Cognitive Impairment using machine learning algorithms”, SIIM Annual Meeting, Maryland.

10. Rohini P, Sundar S, Ramakrishnan S, (2018) “Assessment of Alzheimer Disease Progression using Texture Analysis in Magnetic Resonance Images”, Rocky Mountain Bioengineering Symposium, North Dakota.

11. Rohini P, Sundar S, Ramakrishnan S, (2017) “Analysis of structural alterations of Brainstem in Alzheimer Magnetic Resonance Images using Gaussian mixture model and SVM”, Conference on Recent advances in MRI and MRS, New Delhi.

12. Rohini P, Suhail P. Pathan, Ramakrishnan S, (2017) “Shape Distinction in Human Epithelial Type-2 Cell Images Using Local Symmetry and Random Forest Approaches”, Conference on Machine Intelligence in Medical Imaging , Baltimore.