Dr. Bipul Das Profile

Dr. Bipul Das

at Univ of Pennsylvania Health System

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Publications (2)

Proceedings Article | 29 April 2005 Paper

Cerebrovascular plaque segmentation using object class uncertainty snake in MR images

Bipul Das, Punam Saha, Ronald Wolf, Hee Kwon Song, Alexander Wright, Felix Wehrli

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.596150

KEYWORDS: Image segmentation, Arteries, Magnetic resonance imaging, In vivo imaging, Data modeling, Fuzzy logic, Radiology, Blood, Neck, Image processing algorithms and systems

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Atherosclerotic cerebrovascular disease leads to formation of lipid-laden plaques that can form emboli when ruptured causing blockage to cerebral vessels. The clinical manifestation of this event sequence is stroke; a leading cause of disability and death. In vivo MR imaging provides detailed image of vascular architecture for the carotid artery making it suitable for analysis of morphological features. Assessing the status of carotid arteries that supplies blood to the brain is of primary interest to such investigations. Reproducible quantification of carotid artery dimensions in MR images is essential for plaque analysis. Manual segmentation being the only method presently makes it time consuming and sensitive to inter and intra observer variability. This paper presents a deformable model for lumen and vessel wall segmentation of carotid artery from MR images. The major challenges of carotid artery segmentation are (a) low signal-to-noise ratio, (b) background intensity inhomogeneity and (c) indistinct inner and/or outer vessel wall. We propose a new, effective object-class uncertainty based deformable model with additional features tailored toward this specific application. Object-class uncertainty optimally utilizes MR intensity characteristics of various anatomic entities that enable the snake to avert leakage through fuzzy boundaries. To strengthen the deformable model for this application, some other properties are attributed to it in the form of (1) fully arc-based deformation using a Gaussian model to maximally exploit vessel wall smoothness, (2) construction of a forbidden region for outer-wall segmentation to reduce interferences by prominent lumen features and (3) arc-based landmark for efficient user interaction. The algorithm has been tested upon T1- and PD-weighted images. Measures of lumen area and vessel wall area are computed from segmented data of 10 patient MR images and their accuracy and reproducibility are examined. These results correspond exceptionally well with manual segmentation completed by radiology experts. Reproducibility of the proposed method is estimated for both intra- and inter-operator studies.

Proceedings Article | 12 May 2004 Paper

Object class uncertainty induced snake with applications to medical image segmentation

Bipul Das, Punam Saha, Felix Wehrli

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535547

KEYWORDS: Image segmentation, Medical imaging, Brain, Visualization, Medical imaging applications, Magnetic resonance imaging, 3D modeling, Fuzzy logic, Image processing, Binary data

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