Detection of Uninformative Regions in Wireless Capsule Endoscopy Images

被引:0
|
作者
Alizadeh, Mandi [1 ]
Sharzehi, Kaveh [2 ]
Talebpour, Alireza [3 ]
Soltanian-Zadeh, Hamid [4 ,5 ]
Eskandari, Hoda [6 ]
Maghsoudi, Omid Haji [1 ]
机构
[1] Temple Univ, Dept Bioengn, Philadelphia, PA 19122 USA
[2] Temple Univ, Sch Med, Dept Med, Gastroenterol Sect, Philadelphia, PA 19122 USA
[3] Shahid Beheshti Univ, Dept Elect Engn, Tehran, Iran
[4] Univ Tehran, Sch Elect & Comp Engn, Control & Intelligent Proc Ctr Excellence CIPE, Tehran, Iran
[5] Henry Ford Hlth Syst, Radiol Image Anal Lab, Detroit, MI USA
[6] Shahid Behesti Univ, Dept Radiat Med Engn, Tehran, Iran
关键词
Wireless Capsule Endoscopy; Chan-Vese active contour; Color Range Ratio; segmentation; texture extraction;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Wireless capsule endoscopy (WCE) is able to investigate the entire gastrointestinal tract including the small bowel. To reduce the reviewing time of captured images by gastroenterologists and increasing the accuracy rate for automatic detection of abnormalities, it is beneficial to remove regions which have less or no clinical information of small bowel texture (i.e., uninformative regions). In this research study, a multi -stage method including Chan-Vese, color range ratio, adaptive gamma correction method (AGCM), canny color edge detection operator, and morphological processing is proposed to detect these uninformative regions. The results support the effectiveness of the proposed method. In conclusion, the proposed method is a simple method to implement and performed well in removing the uninformative regions of small bowel images.
引用
收藏
页数:2
相关论文
共 50 条
  • [41] A Novel Method for Automatic Detection ofInflammatory BowelDiseasesin Wireless Capsule Endoscopy Images
    Sindhu, C. P.
    Valsan, Vysak
    2017 FOURTH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATION AND NETWORKING (ICSCN), 2017,
  • [42] A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images
    Jain, Samir
    Seal, Ayan
    Ojha, Aparajita
    Yazidi, Anis
    Bures, Jan
    Tacheci, Ilja
    Krejcar, Ondrej
    COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 137
  • [43] Bleeding Detection in Wireless Capsule Endoscopy Images Based on Binary Feature Vector
    Zhou, Shangbo
    Song, Xinying
    Siddique, Muhammad Abubakar
    Xu, Jie
    Zhou, Ping
    FIFTH INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL AND INFORMATION PROCESSING (ICICIP), 2014, : 29 - 33
  • [44] Enhancing Wireless Capsule Endoscopy Images for Illumination and Noise
    Kadkhodaei, B.
    Hassanpour, H.
    INTERNATIONAL JOURNAL OF ENGINEERING, 2025, 38 (05): : 964 - 975
  • [45] An intelligent compression system for wireless capsule endoscopy images
    Bouyaya, Dallel
    Benierbah, Said
    Khamadja, Mohammed
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 70
  • [46] Multiple bleeding detection in wireless capsule endoscopy
    Bchir, Ouiem
    Ben Ismail, Mohamed Maher
    AlZahrani, Nada
    SIGNAL IMAGE AND VIDEO PROCESSING, 2019, 13 (01) : 121 - 126
  • [47] Multiple bleeding detection in wireless capsule endoscopy
    Ouiem Bchir
    Mohamed Maher Ben Ismail
    Nada AlZahrani
    Signal, Image and Video Processing, 2019, 13 : 121 - 126
  • [48] Polyp Detection in Wireless Capsule Endoscopy Images Using Novel Color Texture Features
    Zhao, Qian
    Meng, Max Q. -H.
    2011 9TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2011), 2011, : 948 - 952
  • [49] An Improved Bleeding Detection Method for Wireless Capsule Endoscopy (WCE) Images Based on AlexNet
    Sunitha, S.
    Sujatha, S. S.
    ICSPC'21: 2021 3RD INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION (ICPSC), 2021, : 11 - 15
  • [50] Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images
    Alaskar, Haya
    Hussain, Abir
    Al-Aseem, Nourah
    Liatsis, Panos
    Al-Jumeily, Dhiya
    SENSORS, 2019, 19 (06)