Dr Sangeeta K Siri
Professor, Department: Electronics and Communication Engineering
Professional Summary
Dr. Sangeeta K Siri is a dedicated professor in the Department of Electronics and Communication Engineering at Global Academy of Technology, with over 22 years of teaching experience. She completed her BE from SDMCET, Dharwad in 1998, followed by an M.Tech from BMSCE, Bengaluru in 2010, and a PhD from VTU, Belagavi in 2019. Her primary area of interest is Image Processing, where she has made significant contributions through her research. Dr. Siri has authored several publications in prestigious journals and conferences, focusing on advanced techniques for image analysis, particularly in medical imaging. She actively engages in student projects and has been recognized with a Best Paper Award at a national conference, reflecting her commitment to academic excellence and innovation in her field.
Academic Qualifications
- P.hD: Medical Image Processing, VTU
- M.Tech: Electronics, VTU
- BE: Electronics and Communication Engineering, Karnataka University
Areas of Interest
- Image Processing
Selected Publications
- Combined endeavor of Neutrosophic Set and Chan-Vese model to extract accurate liver image from CT scan - Q1 (Scopus and Web of Science) https://www.sciencedirect.com/science/article/pii/S0169260717301955
- A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method - Q2 (Scopus and Web of Science) https://www.sciencedirect.com/science/article/pii/S0169260717301955
- Universal Liver Extraction Algorithm: An Improved Chan–Vese Model - Q2 (Scopus and Web of Science) https://www.sciencedirect.com/science/article/pii/S0169260717301955
- Accurate Liver Border Identification Model in CT Scan Images - Q3 (Scopus and Web of Science) https://www.sciencedirect.com/science/article/pii/S0169260717301955
- An Improved Expectation-Maximization Algorithm to Detect Liver Image Boundary in CT Scan Images - Q2 (Scopus and Web of Science) https://www.sciencedirect.com/science/article/pii/S0169260717301955
- Threshold-Based New Segmentation Model to Separate the Liver from CT Scan Images - Q2 (Scopus and Web of Science) https://www.sciencedirect.com/science/article/pii/S0169260717301955
- Volumetric lung nodule segmentation in thoracic CT scan using freehand sketch - Q2 (Scopus and Web of Science) https://www.sciencedirect.com/science/article/pii/S0169260717301955
- Universal Fast Marching Method to Identify Liver Image, IOS Conference (Scopus) https://www.sciencedirect.com/science/article/pii/S0169260717301955
Projects
- KSCST Project - Students' Project
Awards
- Best Paper Award in National Conference on Networking, Embedded and Wireless Systems NEWS 2010
Email