student Profile - NIDHI

About Me

  • Nice Impressive Dedicated Honest Innovative (NIDHI)

Interest Area

  • Medical Imaging, Machine Learning, Computer Vision, CAD systems

EDUCATION

  • 10th Std from St. Anthonys Convent Girls Inter College, Allahabad in year 2003
  • 12th Std from St. Anthonys Convent Girls Inter College, Allahabad in year 2005
  • Graduation from University of Allahabad in year 2010
  • Masters from PDPM IIITDM Jabalpur in year 2013

Curricular Activity



Year/Sem Course Name Course Code Credit

Title: Brain Tumor Detection and Glioma Identification


Start Date: 2013-08-01
Status: On Going
Supervisor: Dr. Pritee Khanna

Descriptiion: Development of CAD systems to assist radiologists and clinicians in real time applications.



Project Id: 94

Title: Image enhancement, tumor detection and identification from brain MRIs

Start Date: End Date: Status: On going
Supervisor: Pritee Khanna

Descriptiion:

Development of CAD systems to assist radiologists and clinicians to diagnose brain tumors and identify its type.



1. Nidhi Gupta, Rajib Jha, Enhancement of dark images using dynamic stochastic resonance with anisotropic diffusion in Journal of Electronic Imaging 2016 vol:25 Num:2 page:123-130 at: Publisher:SPIE, DOI=http://dx.doi.org/10.1117/1.JEI.25.2.023017.

2. Nidhi Gupta, Pritee Khanna, A Fast and Efficient Computer Aided Diagnostic System to Detect Tumor from Brain Magnetic Resonance Imaging in International Journal of Imaging Systems and Technology 2015 vol:25 Num: page:123-130 at: Publisher:Wiley, DOI=http://dx.doi.org/10.1002/ima.22128

3. Nidhi Gupta, Pushpraj Bhatele, Pritee Khanna, Identification of Gliomas from Brain MRIs through Adaptive Segmentation and Run Length of Centralized Patterns in Journal of Computational Science 2017 vol: Num: page:- at: Publisher:Elsevier, DOI=http://dx.doi.org/10.1016/j.jocs.2017.02.009

4. Nidhi Gupta and Pritee Khanna, A non-invasive and adaptive CAD system to detect brain tumor from T2-weighted MRIs using customized Otsu’s thresholding with prominent features and supervised learning in Signal Processing: Image Communication 2017 vol:59 Num: page:18-26 at: Publisher:Elsevier, DOI=http://dx.doi.org/http://dx.doi.org/10.1016/j.image.2017.05.013

1. Nidhi Gupta, Ayan Seal, Pushpraj Bhatele, Pritee Khanna, Selective Block Based Approach for Neoplasm Detection from T2-Weighted Brain MRIs in International Conference on Signal and Image Processing, 2016, pp 151-155, Beijing, China, August 13, 2016- August 15, 2016, IEEE, DOI=http://dx.doi.org/http://dx.doi.org/2016

2. N. Gupta, S. Mishra, and P. Khanna, Classification of Brain MRIs forming Superpixels in ICACCP 2017, 2017, pp -, Sikkim, India, September 8, 2017-September 10, 2017, Springer

1. On 2015-08-01, Hall Representative (Girls) Till date

2. On 2013-08-01, Hall 1 Block A Executive Member

3. On 2013-08-01, Sports Secretary (Hall 1: Girls)

4. On 2013-08-01, Women Cell Member


1. On 2016-09-26, Hindi Pakhwada Poem Writing

2. On 2012-08-01, Jagrati Member (Financial Management)

3. On 2012-03-23, Tarang (Quiz)


Computer Science department IIITDM Jabalpur
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