Content-Based Retrieval and Classification of Ultrasound Medical Images of Ovarian Cysts

  • Abu Sayeed Md. Sohail
  • Prabir Bhattacharya
  • Sudhir P. Mudur
  • Srinivasan Krishnamurthy
  • Lucy Gilbert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5998)


This paper presents a combined method of content-based retrieval and classification of ultrasound medical images representing three types of ovarian cysts: Simple Cyst, Endometrioma, and Teratoma. Combination of histogram moments and Gray Level Co-Occurrence Matrix (GLCM) based statistical texture descriptors has been proposed as the features for retrieving and classifying ultrasound images. To retrieve images, relevance between the query image and the target images has been measured using a similarity model based on Gower’s similarity coefficient. Image classification has been performed applying Fuzzy k-Nearest Neighbour (k-NN) classification technique. A database of 478 ultrasound ovarian images has been used to verify the retrieval and classification accuracy of the proposed system. In retrieving ultrasound images, the proposed method has demonstrated above 79% and 75% of average precision considering the first 20 and 40 retrieved images respectively. Further, 88.12% of average classification accuracy has been achieved in classifying ultrasound images using the proposed method.


Ultrasound Medical Image Retrieval Ovarian Cyst Classification Texture Feature Histogram Moments Fuzzy k-NN 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Abu Sayeed Md. Sohail
    • 1
  • Prabir Bhattacharya
    • 2
  • Sudhir P. Mudur
    • 1
  • Srinivasan Krishnamurthy
    • 3
  • Lucy Gilbert
    • 3
  1. 1.Dept. of Computer Science and Software EngineeringConcordia UniversityCanada
  2. 2.Dept. of Computer ScienceUniversity of CincinnatiOhioUSA
  3. 3.Dept. of Obstetrics and GynecologyRoyal Victoria HospitalMontrealCanada

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