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Abhishek Bal
Researcher at RCC Institute of Information Technology
Publications - 12
Citations - 138
Abhishek Bal is an academic researcher from RCC Institute of Information Technology. The author has contributed to research in topics: Segmentation & Image processing. The author has an hindex of 5, co-authored 12 publications receiving 89 citations. Previous affiliations of Abhishek Bal include Information Technology University.
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Journal ArticleDOI
An Improved Method for Handwritten Document Analysis Using Segmentation, Baseline Recognition and Writing Pressure Detection
Abhishek Bal,Rajib Saha +1 more
TL;DR: This research proposed an off-line handwritten document analysis through segmentation, skew recognition and writing pressure detection for cursive handwritten document through modified horizontal and vertical projection that can segment the text lines and words even if the presence of overlapped and multi-skewed text lines.
Journal ArticleDOI
MRI Brain Tumor Segmentation and Analysis using Rough-Fuzzy C-Means and Shape Based Properties
TL;DR: Experimental results show that the proposed automated brain tumor segmentation method using rough-fuzzy C-means (RFCM) has achieved better performance based on statistical volume metrics than previous state-of-the-art algorithms with respect to ground truth (manual segmentation).
Proceedings ArticleDOI
Brain Tumor Segmentation on MR Image Using K-Means and Fuzzy-Possibilistic Clustering
TL;DR: A method for brain tumor segmentation from MR images is proposed which is based on fuzzy-possibilistic C-means (FPCM) and shape based topological properties to identify the exact tumor region.
Journal ArticleDOI
An efficient wavelet and curvelet-based PET image denoising technique.
TL;DR: This research proposes an efficient PET image denoising technique based on the combination of wavelet and curvelet transforms, along with a new adaptive threshold selection to threshold the wavelet coefficients in each subband (except last level low pass (LL) residual).
Journal ArticleDOI
An efficient method for PET image denoising by combining multi-scale transform and non-local means
TL;DR: An efficient denoising framework for reducing the noise level of brain PET images based on the combination of multi-scale transform (wavelet and curvelet) and tree clustering non-local means (TNLM).