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首页模糊距离变换在肺裂分割技术中的应用
模糊距离变换在肺裂分割技术中的应用
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更新于2024-08-26
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"基于模糊距离变换的肺裂分割方法是一种创新的医学图像处理技术,用于在肺部CT或MRI图像中精确识别肺叶之间的边界,即肺裂。该方法结合了模糊逻辑和距离变换的概念,提高了分割的准确性和可靠性,对于临床诊断具有重要意义。 文章由苏高和刘王共同撰写,分别来自北京师范大学大学医院和沈阳航空航天大学计算机学院。他们提出的新方法首先应用隶属度函数和阈值来处理图像,以模糊边界的方式分割出肺裂的轮廓。这种结合方式可以更好地处理图像中的不确定性,适应肺裂边缘的不清晰情况。 接下来,他们利用模糊距离变换(FDT)对轮廓图像进行操作。FDT能够揭示图像中的结构信息,其脊线位置能够准确指示出肺裂的位置。在FDT图像中,肺裂表现为明显的脊线特征。为了定位裂隙的关键点,研究者结合了FDT图像的局部最大值和局部最小梯度信息。这些关键点作为肺裂的起点和终点,是连接连续肺裂的重要依据。 最后,通过追踪FDT图像中最大邻域点,可以建立起连续的肺裂路径。这种方法有效地解决了肺裂分割中的断裂和不连续性问题,使得分割结果更加连贯且符合实际解剖结构。 关键词:肺裂分割;模糊距离变换;最大邻域点" 该研究引入的模糊距离变换技术在肺裂分割中具有显著优势,尤其在处理图像噪声和边界模糊的情况下,能够提供更准确的分割结果。这一方法不仅对临床医生进行肺部疾病的诊断提供了有力支持,也为后续的肺部疾病分析和治疗规划提供了可靠的数据基础。此外,该技术还可以进一步扩展到其他医学图像分割问题,如血管、肿瘤等的分割,对整个医学成像领域具有广泛的潜在应用价值。
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Method of pulmonary fissure segmentation based on fuzzy distance transform
Su Gao
University Hospital
Beijing Normal University
Beijing, China
E-mail: gaosu@bnu.edu.cn
Liu Wang
School of Computer
Shenyang Aerospace University
Shenyang 110136, China
E-mail:22659488@qq.com
Abstract—A pulmonary fissure is a boundary between the
lobes in the pulmonary. The segmentation of it plays an
important role in the clinical diagnosis. This paper describes a
new method for the segmentation of pulmonary fissures. A
membership function and a threshold are firstly combined to
segment the contour of pulmonary fissures with fuzzy
boundaries. Then, fuzzy distance transform (FDT) is operated
on the contour image, and the ridges of FDT image accurately
indicate the pulmonary fissures. The key points of fissure are
determined by combining the local maximum of FDT image
and the local minimum gradient of FDT images. Finally, a
continuous pulmonary fissure is obtained by tracking the
maximal neighborhood points.
Keywords: Pulmonary fissure segmentation; Fuzzy
distance transform; Maximal neighborhood point
I. INTRODUCTION
With the emergence of new digital imaging technology,
images are widely used in various fields, and medical
images have a great advantage in diagnosis and treatment.
The computer-aided diagnostic (CAD) system is employed
to assist radiologists in their routine work. It automatically
analyzes CT images and achieves results of diagnosis for
many diseases.
The identification of fissures is crucial for
understanding the anatomy of pulmonary in CT images.
Pulmonary consists of left and right parts. The left
pulmonary has two lobes separated by a major fissure, and
the right one has three lobes separated by two fissures.
There are mainly two problems in the segmentation of
pulmonary fissures by use of thresholding[1,2]: (1) Because
the fissure is a tiny structure in the pulmonary, it is difficult
to find a suitable threshold to segment it. (2) A high noise
level with low contrast would cause a low performance for
the segmentation of pulmonary fissures.
Segmentation for fissures and lobes has attracted a large
of interest over the last few years, which are addressed in
refs.3-6. This paper proposes a new method to detect the
pulmonary fissures based on the fuzzy distance transform
and maximal neighborhood points tracking. Our method
firstly searches the contour of fissure by combining a
membership function and a threshold. Then, the locations
of the main points of fissure are determined by fuzzy
distance transform (FDT). Finally, the main points are
connected by tracking the maximal neighborhood points.
II. FUZZY DISTANCE TRANSFORM
Distance transform (DT) is widely used in target
recognition and image processing. For a binary object, DT
is a process that assigns a value to each location within the
object, which is the shortest distance between the location
and background. However, DT cannot be applied to fuzzy
objects effectively. The notion of DT for a fuzzy object is
called fuzzy distance transform (FDT)[7]. FDT is operated
on a gray image, and it considers the intensity and distance
simultaneously. FDT can be applied in more fields by use
of a membership function, and it is a minimum length of
path between two points in a fuzzy set[8].
Rutovitz proposes a notion of gray-weighted distance in
1968. Later, Saha develops FDT in terms of it. FDT
describes the length of the shortest path between the point p
and q. We will give you some related knowledge about
FDT.
A. Fuzzy subset and membership function
Zadeh proposes a definition of a fuzzy set in 1965. Let
X is a set, and a fuzzy subset A of X is defined as:
{( , ( )) | }, ( ) : [0,1]AAAxxxX xX
μμ
=∈ →
(1)
Where
()A
x
μ
is the membership function of A.
B. Fuzzy distance between adjacent points
A distance between two adjacent point s p and q in a
fuzzy subset is no longer calculated in terms of Euclidean
distance. A path
π
in a set A from p to q consists of a
sequent points
1, 2,,mppp p q=="
. Where i
p
A∈
,
1 im≤≤
.
j
p
is next to
1j
p
+
.The length of these two
adjacent points is defined as:
() ()()
qpqp −×+ ΟΟ
μμ
2
1
(2)
Where
⋅
denotes the spatial Euclidean distance. In our
study, we used the
1, 2
distance. If p and q are neighbors
in the vertical direction,
⋅
is equal to 1. If they are vertex
neighbors,
⋅
is equal to
2
. We also can use other
2014 International Conference on Virtual Reality and Visualization
978-1-4799-6854-1/14 $31.00 © 2014 IEEE
DOI 10.1109/ICVRV.2014.60
339
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