iPixel: A visual content-based and semantic search engine for retrieving digitized mammograms by using collective intelligence

Abstract

Nowadays, traditional search engines such as Google, Yahoo and Bing facilitate the retrieval of information in the format of images, but the results are not always useful for the users. This is mainly due to two problems: (1) the semantic keywords are not taken into consideration and (2) it is not always possible to establish a query using the image features. This issue has been covered in different domains in order to develop content-based image retrieval (CBIR) systems. The expert community has focussed their attention on the healthcare domain, where a lot of visual information for medical analysis is available. This paper provides a solution called iPixel Visual Search Engine, which involves semantics and content issues in order to search for digitized mammograms. iPixel offers the possibility of retrieving mammogram features using collective intelligence and implementing a CBIR algorithm. Our proposal compares not only features with similar semantic meaning, but also visual features. In this sense, the comparisons are made in different ways: by the number of regions per image, by maximum and minimum size of regions per image and by average intensity level of each region. iPixel Visual Search Engine supports the medical community in differential diagnoses related to the diseases of the breast. The iPixel Visual Search Engine has been validated by experts in the healthcare domain, such as radiologists, in addition to experts in digital image analysis.

Keywords:

• CBIR
• collective intelligence
• iPixel
• mammograms

Acknowledgements

The authors give special thanks to The mini-MIAS database of mammograms (http://peipa.essex.ac.uk/info/mias.html) for providing all digitized mammograms used by iPixel. iPixel follows the licence agreement established by the MIAS on the use of the mammograms database for research purposes.

Declaration of Interest: The authors report no conflicts of interest.

This work was supported by the General Council of Superior Technological Education of Mexico (DGEST). Additionally, this work was sponsored by the National Council of Science and Technology (CONACYT) and the Public Education Secretary (SEP) through PROMEP (Teacher Improvement Program or Programa de Mejoramiento del Profesorado, in Spanish).