Conference Paper2015Cited by 8

Teaching & Learning System for Diagnostic Imaging-Phase I: X-Ray Image Analysis & Retrieval.

MS Shahriar Faruque, Shourav Banik, Mahmood Kazi Mohammed, Mahady Hasan, M Ashraful Amin

CSEDU (1)

SCITEPRESS - Science and and Technology Publications, pp. 430–435

  • Good Health and Well-being — Relevance: 79%
  • Quality Education — Relevance: 92%
DOI: 10.5220/0005479604300435Open AccessURL

Abstract

This paper presents a framework for building diagnostic imaging teaching and learning facility for entry level medical students of Bangladesh. Initially we demonstrate an X-Ray image analysis and retrieval system that will work as one of the main component in this system. This web based system has three modes. First is the annotation mode where an expert radiologist manually performs annotation of raw x-ray images. To aid the annotation process proposed model proposes a manual and a semi-auto segmentation tool in identifying the region of interests (ROI) in the X-Ray images. Image Retrieval in Medical Applications (IRMA) structure has been used for the annotating the ROIs. In the learning mode, students can retrieve images from the database created by expert radiologists. We proposed information retrieval techniques to find x-ray images of interest. We have used text based and content based search methods which is based on term frequency–inverse document frequency (tf-idf), and Gabor filter respectively.

Keywords

  • TF-IDF
  • Gabor filters
  • medical image retrieval
  • region of interest segmentation
  • content-based image retrieval
  • radiology education
  • X-ray image analysis
  • text-based image retrieval
  • web-based learning system
  • medical students

CCDS Authors

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

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Cited By

  1. 1.Mauricio Villegas, Henning Müller, Andrew Gilbert, Luca Piras, Josiah Wang, Krystian Mikolajczyk, Alba G. Seco de Herrera, Stefano Bromuri, M. Ashraful Amin, Mahmood Kazi Mohammed, Burak Acar, Suzan Uskudarli, Neda B. Marvasti, José F. Aldana, María del Mar Roldán García. (2015). General Overview of ImageCLEF at the CLEF 2015 Labs. Lecture notes in computer science, 444–461[10.1007/978-3-319-24027-5_45]
  2. 2.M. Ashraful Amin, Mahmood Kazi Mohammed. (2015). Overview of the ImageCLEF 2015 medical clustering task. CLEF (Working Notes)
  3. 3.Mauricio Villegas, Henning Müller, Alba García Seco de Herrera, Roger Schaer, Stefano Bromuri, Andrew Gilbert, Luca Piras, Josiah Wang, Fei Yan, Arnau Ramisa, Emmanuel Dellandrea, Robert Gaizauskas, Krystian Mikolajczyk, Joan Puigcerver, Alejandro H. Toselli, Joan-Andreu Sánchez, Enrique Vidal. (2016). General Overview of ImageCLEF at the CLEF 2016 Labs. Lecture notes in computer science, 267–285[10.1007/978-3-319-44564-9_25]
  4. 4.David Lyndon, Ashnil Kumar, Jinman Kim, Philip Heng Wai Leong, Dagan Feng. (2015). Convolutional Neural Networks for Medical Clustering. CLEF (Working Notes)
  5. 5.Sungbin Choi. (2015). X-ray image body part clustering using deep convolutional neural network: SNUMedinfo at ImageCLEF 2015 medical clustering task. CLEF (Working Notes)
  6. 6.Sanad Saha, Asif Mahmud, Amin Ahsan Ali, Md. Ashraful Amin. (2016). Classifying digital X-ray images into different human body parts. , 67–71[10.1109/iciev.2016.7760190]
  7. 7.Ibrahim Ziedan, Amr A. Zamel, Ahmed Al Zohairy. (2015). Clustering of Medical X-ray Images by Merging Outputs of Different Classification Techniques.. CLEF (Working Notes)
  8. 8.Suman Sedai, Xi Liang, Mani Abedini, Qiang Chen, Rajib Chakravorty, Rahil Garnavi. (2015). IBM Research at Image CLEF 2015: Medical Clustering Task.. CLEF (Working Notes)
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