Retrieval and classification of ultrasound images of ovarian cysts combining texture features and histogram moments

  • Authors:

  • Abu Sayeed Md. Sohail;Md. Mahmudur Rahman;Prabir Bhattacharya;Srinivasan Krishnamurthy;Sudhir P. Mudur

  • Affiliations:

  • Dept. of Computer Science and Software Engineering, Concordia University, Montreal, Canada;U.S. National Library of Medicine, National Institutes of Health, Bethesda, Maryland;Dept. of Computer Science, University of Cincinnati, Cincinnati, Ohio;Dept. of Obstetrics and Gynecology, Royal Victoria Hospital, Montreal, Canada;Dept. of Computer Science and Software Engineering, Concordia University, Montreal, Canada

  • Venue:
  • ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
  • Year:
  • 2010

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Abstract

This paper presents an effective solution for content-based retrieval and classification of ultrasound medical images representing three types of ovarian cysts: Simple Cyst, Endometrioma, and Teratoma. Our proposed solution comprises of the followings: extraction of low level ultrasound image features combining histogram moments with Gray Level Co-Occurrence Matrix (GLCM) based statistical texture descriptors, image retrieval using a similarity model based on Gower's similarity coefficient which measures the relevance between the query image and the target images, and use of multiclass Support Vector Machine (SVM) for classifying the low level ultrasound image features into their corresponding high level categories. Efficiency of the above solution for ultrasound medical image retrieval and classification has been evaluated using an inprogress database, presently consisting of 478 ultrasound ovarian images. Performance-wise, in retrieval of ultrasound images, our proposed solution has demonstrated above 77% and 75% of average precision considering the first 20 and 40 retrieved results respectively, and an average classification accuracy of 86.90%.