Dr. P. Mirunalini
B.E., M.E., Ph.D
Associate Professor
Department Of Computer Science and Engineering
Extn : 362
Dr. P. Mirunalini, Associate Professor in the Department of Computer Science and Engineering at SSN College of Engineering, Chennai, India, since May 2005. She has 4 years of Industrial Experience and 18 years of teaching and research experience. She received her Ph.D from Anna University, Chennai in 2016. She obtained her Master’s degree in Multimedia Technology with First class and Distinction from College of Engineering, Guindy in 2005. She completed BE in CSE from University of Madras in 1998.
She is a member of the Machine Learning Research Group of SSN and she has been consistent in taking part on shared tasks introduced under CLEF initiative labs to promote the evaluation of technologies for annotation, indexing, classification and retrieval of multimodal data.
One of her paper was awarded “Best paper award” at an international conference. She has published 9 papers in journals, 21 papers in international conferences and 4 papers in national conferences.
Medical image processing and Analysis, Image reconstruction, Machine learning , Deep learning, Pattern Recognition
- Mirunalini, P., Aravindan, C. & Jaisakthi, S.M.,” Automatic stenosis detection using SVM from CTA projection images”, Multimedia Systems (2019), 25-2: 83. https://doi.org/10.1007/s00530-017-0578-1. Spinger Publisher, IF:2.207/2017
- Seetharani Murugaiyan Jaisakthi; Palaniappan Mirunalini; Chandrabose Aravindan, “Automated skin lesion segmentation of dermoscopic images using GrabCut and k-means algorithms”, IET Computer Vision, Volume 12, Issue 8, December 2018, p. 1088 – 1095, DOI:1049/iet-cvi.2018.5289, Print ISSN 1751-9632, Online ISSN 1751-9640.
- Mirunalini, C. Aravindan, A. TamilNambi, S. Poorvaja and PoojaPriya, “Segmentation of Coronary Arteries from CTA axial slices using Deep Learning echniques,” TENCON 2019 – 2019 IEEE Region 10 Conference (TENCON), Kochi, India, 2019, pp. 2074-2080.
- Jaisakthi. S. M, Mirunalini. P, Aravindan. C, Rajagopal Appavu, “Classification of Skin Cancer from Dermoscopic Images using Deep Neural Network Architectures”, in Multimedia Tools and Applications, Oct 2022,(Clarivate IF:2.57)
- S. M. Jaisakthi, Karthik Desingu, P. Mirunalini, S. Pavya & N. Priyadharshini, “A deep learning approach for nucleus segmentation and tumor classification from lung histopathological images”, Network Modeling Analysis in Health Informatics and Bioinformatics, Vol.12, No.22, May 2023, IF : 2.045
Dwivedi, Vatsala P., Mirunalini S., Ramani S., M. Jaisakthi, “A process for grape leaf disease identification using machine learning techniques”, Australian Patent (patent No: AU2021101496A4)
The following projects were funded by SSN Trust:
- Automatic Detection of Stenosis in Computed Tomography Angiography (CTA) Images of Heart using Deep Learning Techniques. Rs. 3 lakh
- Automatic Number Plate Recognition- Rs.0.15 lakh
- Multi-Level Smart Parking System – Rs.0.15 lakh
- Self Defence System for Children – Rs.0.13 lakh
- IoT Driven Smart Bus Stops – Rs.0.12 lakh
- IoT driven non-invasive diabetes detecting system – Rs.0.14 lakh
- Development of a controlled wall climbing robot for aid in construction, wall analysis and inspection – Rs.0.30 lakh
- Image Processing software using Stereo vision technology for the measurement of the wheel Angles and Distances, Manatec, Puducherry
- Optimised surface preparation for building construction, Caterpillar India Pvt. Ltd, Chennai, India
- 3D reconstruction for photogrammetry, Vimana Technologies, Bengaluru
CSI, IEEE and ACM.