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A Level Set based Unified Framework for Pulmonary Nodule Segmentation
IEEE Signal Processing Letters ( IF 3.9 ) Pub Date : 2020-01-01 , DOI: 10.1109/lsp.2020.3016563
Rukhmini Roy , Pranavesh Banerjee , Ananda S. Chowdhury

This letter introduces a unified framework for accurate segmentation of five different types of pulmonary nodules, namely, solid, juxtapleural, juxtavascular, part solid and ground glass by designing a contrast-adaptive shape-driven level set algorithm. Most of the existing methods have targeted segmenting few specific types of nodules. Variability of shapes along with poor contrast make pulmonary nodule segmentation an extremely challenging problem. To deal with low contrast, a contrast-adaptive term, based on intensities, is incorporated to guide the evolution of level set. A shape term is further introduced for accurate segmentation of different pulmonary nodules having varying shapes. Experiments on the publicly available LIDC/IDRI dataset clearly reveal that our method achieves promising results as compared to several state-of-the-art competitors.

中文翻译:

基于水平集的肺结节分割统一框架

这封信通过设计一种对比度自适应形状驱动的水平集算法,介绍了一个统一的框架,用于准确分割五种不同类型的肺结节,即实性、胸膜旁、血管旁、部分实性和毛玻璃。大多数现有方法都针对分割少数特定类型的结节。形状的可变性以及较差的对比度使肺结节分割成为一个极具挑战性的问题。为了处理低对比度,引入了一个基于强度的对比度自适应术语来指导水平集的演变。进一步引入形状项以准确分割具有不同形状的不同肺结节。
更新日期:2020-01-01
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