Content-based retrieval and classification of ultrasound medical images of ovarian cysts

  • Authors:

  • Abu Sayeed Md. Sohail;Prabir Bhattacharya;Sudhir P. Mudur;Srinivasan Krishnamurthy;Lucy Gilbert

  • Affiliations:

  • Dept. of Computer Science and Software Engineering, Concordia University, Canada;Dept. of Computer Science, University of Cincinnati, Ohio;Dept. of Computer Science and Software Engineering, Concordia University, Canada;Dept. of Obstetrics and Gynecology, Royal Victoria Hospital, Montreal, Canada;Dept. of Obstetrics and Gynecology, Royal Victoria Hospital, Montreal, Canada

  • Venue:
  • ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
  • Year:
  • 2010

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Abstract

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.