P
Punam K. Saha
Researcher at University of Iowa
Publications - 245
Citations - 8620
Punam K. Saha is an academic researcher from University of Iowa. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 47, co-authored 233 publications receiving 7771 citations. Previous affiliations of Punam K. Saha include University of Pennsylvania & Indian Statistical Institute.
Papers
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Scale-Based Fuzzy Connected Image Segmentation
TL;DR: It is argued that scale-based affinity, and hence connectedness, is natural in object definition and demonstrates that this leads to more effective object segmentation.
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A survey on skeletonization algorithms and their applications
TL;DR: A comprehensive survey of skeletonization algorithms and their applications and their principles, challenges, and benefits are presented.
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CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)
Chenyu You,Wenxiang Cong,Michael W. Vannier,Punam K. Saha,Eric A. Hoffman,Ge Wang,Guang Li,Yi Zhang,Xiaoliu Zhang,Hongming Shan,Mengzhou Li,Shenghong Ju,Zhen Zhao,Zhuiyang Zhang +13 more
TL;DR: Wang et al. as mentioned in this paper proposed a semi-supervised deep learning approach to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of the Wasserstein distance.
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CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Chenyu You,Guang Li,Yi Zhang,Xiaoliu Zhang,Hongming Shan,Shenghong Ju,Zhen Zhao,Zhuiyang Zhang,Wenxiang Cong,Michael W. Vannier,Punam K. Saha,Ge Wang +11 more
TL;DR: In this article, a semi-supervised deep learning approach was proposed to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
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Digital Topological Analysis of In Vivo Magnetic Resonance Microimages of Trabecular Bone Reveals Structural Implications of Osteoporosis
TL;DR: These data provide the first in vivo evidence for the structural implications inherent in postmenopausal osteoporosis accompanying bone loss, that is, the conversion of trabecular plates to rods and disruption of rods due to repeated osteoclastic resorption.