Semi-Automatic Detection of Cervical Vertebrae in X-ray Images Using Generalized Hough Transform

被引：0

作者：

Larhmam, Mohamed Amine ^{[1
]}

Mahmoudi, Said ^{[1
]}

Benjelloun, Mohammed ^{[1
]}

机构：

[1] Univ Mons, Fac Engn, B-7000 Mons, Belgium

来源：

2012 3RD INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS | 2012年

关键词：

Medical imaging; Vertebrae detection; X-ray image; Hough Transform; Template matching;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Vertebra detection presents the first step of any automatic spinal column diagnosis. This task becomes more difficult in the case of the cervical X-ray images characterized by their low contrasts and noise due to skull bones. In this paper, we describe an efficient modified template matching method for detecting cervical vertebrae using Generalized Hough Transform (GHT). The proposed method consists of three main steps toward vertebrae detection: 1) Offline training to obtain a robust average model of cervical vertebra. 2) Detecting the potential vertebra centers. 3) Adaptive Post-processing filter. X-ray Image data of 40 healthy cases were used to validate our approach by using a total of 200 cervical vertebrae. We obtained an accuracy of 89%.

引用

页码：396 / 401

页数：6

共 50 条

[41] FEATURE-AWARE PROHIBITED ITEMS DETECTION FOR X-RAY IMAGES
Liao, Hongyu
Huang, Bin
Gao, Hongxia
2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, 2023, : 1040 - 1044
[42] QUANTITATIVE ANALYSIS AND FRACTURE DETECTION OF PELVIC BONE X-RAY IMAGES
Vijayakumar, R.
Gireesh, G.
2013 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATIONS AND NETWORKING TECHNOLOGIES (ICCCNT), 2013,
[43] Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm
Farhan, Abobaker Mohammed Qasem
Yang, Shangming
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (25) : 38561 - 38587
[44] YOLO-based Threat Object Detection in X-ray Images
Galvez, Reagan L.
Dadios, Elmer P.
Bandala, Argel A.
Vicerra, Ryan Rhay P.
2019 IEEE 11TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT, AND MANAGEMENT (HNICEM), 2019,
[45] Automatic Quantification of Hip Osteoarthritis from Low-Quality X-ray Images
Gutierrez, Giancarlos C.
Castro, Eveling G.
Delgado, Javier O.
Pauca, Paul
Klein, Stefan
Lamecker, Hans
MEDICAL IMAGING 2020: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING, 2021, 11317
[46] X-ray Image Edge Detection Based on Wavelet Transform and Lipschitz Exponent
Tang, Guowei
Zhong, Xiaoqing
Zhang, Fangzhou
Jin, Zhenying
IITSI 2009: SECOND INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY AND SECURITY INFORMATICS, 2009, : 195 - +
[47] Bone Cancer Detection and Classification Using Owl Search Algorithm With Deep Learning on X-Ray Images
Alabdulkreem, Eatedal
Saeed, Muhammad Kashif
Alotaibi, Saud S.
Allafi, Randa
Mohamed, Abdullah
Hamza, Manar Ahmed
IEEE ACCESS, 2023, 11 : 109095 - 109103
[48] FocusCovid: automated COVID-19 detection using deep learning with chest X-ray images
Tarun Agrawal
Prakash Choudhary
Evolving Systems, 2022, 13 : 519 - 533
[49] FocusCovid: automated COVID-19 detection using deep learning with chest X-ray images
Agrawal, Tarun
Choudhary, Prakash
EVOLVING SYSTEMS, 2022, 13 (04) : 519 - 533
[50] A robust crack filter based on local gray level variation and multiscale analysis for automatic crack detection in X-ray images
Peng S.-H.
Nam H.-D.
Nam, Hyun-Do (hdnam@dankook.ac.kr), 1600, Korean Institute of Electrical Engineers (11): : 1037 - 1043

← 1 2 3 4 5 →