Abolfazl Lakdashti - Ph.D. Student of Computer Engineering, Islamic Azad University, Research and Science Branch, Tehran, Iran
Mohammad Shahram Moin - Assistant Professor and Director of Multimedia Research Group, IT Faculty, Iran Telecom Research Center, Tehran, Iran
Kambiz Badie - Associate Professor and Director of IT Faculty, Iran Telecom Research Center, Tehran, Iran

Introduction: Content Based Image Retrieval (CBIR) is a method of image searching and retrieval in a  database. In medical applications, CBIR is a tool used by physicians to compare the previous and current  medical images associated with patients pathological conditions. As the volume of pictorial information  stored in medical image databases is in progress, efficient image indexing and retrieval is increasingly  becoming a necessity.  Materials and Methods: This paper presents a new content based radiographic image retrieval approach  based on histogram of pattern orientations, namely pattern orientation histogram (POH). POH represents  the  spatial  distribution  of  five  different  pattern  orientations:  vertical,  horizontal,  diagonal  down/left,  diagonal down/right and non-orientation. In this method, a given image is first divided into image-blocks  and  the  frequency  of  each  type  of  pattern  is  determined  in  each  image-block.  Then,  local  pattern  histograms for each of these image-blocks are computed.   Results: The method was compared to two well known texture-based image retrieval methods: Tamura  and  Edge  Histogram  Descriptors  (EHD)  in  MPEG-۷  standard.  Experimental  results  based  on  ۱۰۰۰۰  IRMA  radiography  image  dataset,  demonstrate  that  POH  provides  better  precision  and  recall  rates  compared to Tamura and EHD. For some images, the recall and precision rates obtained by POH are,  respectively, ۴۸% and ۱۸% better than the best of the two above mentioned methods.    Discussion and Conclusion: Since we exploit the absolute location of the pattern in the image as well as  its global composition, the proposed matching method can retrieve semantically similar medical images.

Radiographic Image Retrieval, Texture Feature, Pattern Orientation Histogram