An efficient ACL segmentation method

Yun Bo Rao; Xian Shu Ding; Jianping Gou; Ying Ma

doi:10.1007/978-981-10-0539-8_6

An efficient ACL segmentation method

Yun Bo Rao^*
, Xian Shu Ding
, Jianping Gou
, Ying Ma

^*Corresponding author for this work

University of Electronic Science and Technology of China
Jiangsu University
Xiamen University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Soft-tissue segmentation has always been difficult point in the medical research of diagnosis of soft-tissue defects. Especially for Anterior Cruciate Ligament (ACL) rebuilding surgery, ACL segmentation from all soft-tissue inside knee joint, including Posterior Cruciate Ligament (PCL) and meniscus, is a very important task. In this paper, we propose a novel ACL segmentation method: Space Model Contrast Clustering-based (SMC-based) ACL Segmentation. Unlike the widely used processing method, such as segmentation by MRI gray values and Mimics segmentation drawing, the proposed method relies 3D model of knee joint to segment soft tissue by self-adaptive K-means clustering. Extensional experiments demonstrate that the proposed method can be capable of solving the problem of soft-tissue segmentation well and has achieved higher ACL segmentation efficiency.

Original language	English
Title of host publication	Lecture Notes in Electrical Engineering
Publisher	Springer Verlag
Pages	43-52
Number of pages	10
DOIs	https://doi.org/10.1007/978-981-10-0539-8_6
State	Published - 2016
Externally published	Yes

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	375
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Keywords

Anterior cruciate ligament
SMC-based ACL segmentation
Self-adaptive K-means clustering
Soft-tissue segmentation

Access to Document

10.1007/978-981-10-0539-8_6

Cite this

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title = "An efficient ACL segmentation method",

abstract = "Soft-tissue segmentation has always been difficult point in the medical research of diagnosis of soft-tissue defects. Especially for Anterior Cruciate Ligament (ACL) rebuilding surgery, ACL segmentation from all soft-tissue inside knee joint, including Posterior Cruciate Ligament (PCL) and meniscus, is a very important task. In this paper, we propose a novel ACL segmentation method: Space Model Contrast Clustering-based (SMC-based) ACL Segmentation. Unlike the widely used processing method, such as segmentation by MRI gray values and Mimics segmentation drawing, the proposed method relies 3D model of knee joint to segment soft tissue by self-adaptive K-means clustering. Extensional experiments demonstrate that the proposed method can be capable of solving the problem of soft-tissue segmentation well and has achieved higher ACL segmentation efficiency.",

keywords = "Anterior cruciate ligament, SMC-based ACL segmentation, Self-adaptive K-means clustering, Soft-tissue segmentation",

author = "Rao, \{Yun Bo\} and Ding, \{Xian Shu\} and Jianping Gou and Ying Ma",

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doi = "10.1007/978-981-10-0539-8\_6",

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series = "Lecture Notes in Electrical Engineering",

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T1 - An efficient ACL segmentation method

AU - Rao, Yun Bo

AU - Ding, Xian Shu

AU - Gou, Jianping

AU - Ma, Ying

N1 - Publisher Copyright: © Springer Science+Business Media Singapore 2016.

PY - 2016

Y1 - 2016

N2 - Soft-tissue segmentation has always been difficult point in the medical research of diagnosis of soft-tissue defects. Especially for Anterior Cruciate Ligament (ACL) rebuilding surgery, ACL segmentation from all soft-tissue inside knee joint, including Posterior Cruciate Ligament (PCL) and meniscus, is a very important task. In this paper, we propose a novel ACL segmentation method: Space Model Contrast Clustering-based (SMC-based) ACL Segmentation. Unlike the widely used processing method, such as segmentation by MRI gray values and Mimics segmentation drawing, the proposed method relies 3D model of knee joint to segment soft tissue by self-adaptive K-means clustering. Extensional experiments demonstrate that the proposed method can be capable of solving the problem of soft-tissue segmentation well and has achieved higher ACL segmentation efficiency.

AB - Soft-tissue segmentation has always been difficult point in the medical research of diagnosis of soft-tissue defects. Especially for Anterior Cruciate Ligament (ACL) rebuilding surgery, ACL segmentation from all soft-tissue inside knee joint, including Posterior Cruciate Ligament (PCL) and meniscus, is a very important task. In this paper, we propose a novel ACL segmentation method: Space Model Contrast Clustering-based (SMC-based) ACL Segmentation. Unlike the widely used processing method, such as segmentation by MRI gray values and Mimics segmentation drawing, the proposed method relies 3D model of knee joint to segment soft tissue by self-adaptive K-means clustering. Extensional experiments demonstrate that the proposed method can be capable of solving the problem of soft-tissue segmentation well and has achieved higher ACL segmentation efficiency.

KW - Anterior cruciate ligament

KW - SMC-based ACL segmentation

KW - Self-adaptive K-means clustering

KW - Soft-tissue segmentation

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DO - 10.1007/978-981-10-0539-8_6

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T3 - Lecture Notes in Electrical Engineering

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EP - 52

BT - Lecture Notes in Electrical Engineering

PB - Springer Verlag

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An efficient ACL segmentation method

Abstract

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