Dibash BasukalaNYU Langone Health · Department of Radiology
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October 2017 - March 2021
- PhD Researcher
- MRI Image Segmentation, Image Feature Extraction, Medical Image Analysis, Data Analysis, Machine Learning, Brain Imaging and its application to Parkinson's Disease
Accurate segmentation of substantia nigra (SN) and red nucleus (RN) is challenging, yet important for understanding health problems like Parkinson's disease (PD). This paper proposes an algorithm to segment SN and RN from quantitative susceptibility mapping (QSM) MRI and use the results to investigate PD. Algorithm-derived segments (based on level...
Image segmentation is an important step in most medical image analysis tasks. An effective image segmentation method helps clinicians and patients in image-guided surgery, radiotherapy, early disease detection, volumetric measurement, and three-dimensional visualization. The fuzzy c-means (FCM) clustering algorithm is one of the most popular method...
Watershed transformation is an effective segmentation algorithm that originates from the mathematical morphology field. This algorithm is widely used in medical image segmentation because it produces complete division even under poor contrast. However, over-segmentation is its most significant limitation. Therefore, this article proposes a combinat...
Watershed Transformation is a popular segmentation method coming from the field of mathematical morphology. Different kernels such as rice, wheat, and corn are over-segmented by the traditional watershed algorithm. Therefore, this paper proposes an improved watershed segmentation algorithm by automatic selection of threshold value using moment pres...