Heuristic Search Based Parameterized Level Set for Automated Lung Parenchyma Segmentation and Nodule Extraction
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Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh
Abstract
Image Segmentation is a very important image processing technique now a
days. It is greatly used in the sector of medical image processing. Segmentation
for lung areas from CT images is important task on understanding tissue
construction, computing and extracting abnormal areas as well as parenchyma
segmentation. There are many application of the lung image segmentation in
lung parenchyma segmentation, lung nodule extraction, lung tumor classification,
lung cancer detection and so on. These segmentation techniques, some of
them are semi-automatic and some of them are fully-automatic. Some fullyautomatic
techniques include thresholding, snakes, level set, region growing,
bayesian network, hierarchical multi-scale, gradient descent and so on.
The main objectives of the lung image segmentation are the efficiently and
accurately segment the lung parenchyma and the lung nodule. The above mentioned
techniques have their strength in their own dataset to segment correctly
but they cannot perform well in all kinds of dataset. Another thing which are
very important here to reduce time complexity, memory space and calculation
complexity. In this paper we proposed a method which use the bi-directional
chain method to select the seed points near the lung parenchyma automatically.
It helps our next step of the proposed method- parameterized level set
method.
ii
For level set method it is necessary to select random points in the image
through which it converges to the boundary of the object(s). But in our proposed
method we do not use the random seed points. Rather we use the particular
seed points near the lung parenchyma got from bi-directional chain method
in which we use uninformed heuristic search and memoization technique. Then
we implement the level set method to get the lung parenchyma correctly in reduced
computational time. It perfectly segment out the lung parenchyma along
with any irregular boundary and abnormal shape. Next step of our proposed
method is to segment out the lung nodule accurately even if it resides near the
boundary and having any abnormal shape. Experiment is performed employing
60 CT image sets from 18 patients and satisfactory results are obtained. Obtained
results are shown along with a discussion.
Description
Supervised by
Dr. Md. Hasanul Kabir,
Associate Professor,
Department of Computer Science and Engineering,
Co-Supervisor:
Mir Rayat Imtiaz Hossain,
Lecturer,
Department of Computer Science and Engineering
Islamic University of Technology(IUT)
Keywords
Image segmentation, level set method, bi-directional chain encoding, memoization, uninformed heuristic search, morphological operator, nodule extraction.
Citation
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