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iPixel: a visual content-based and semantic search engine for retrieving digitized mammograms by using collective
Giner Alor-Hernández1, Yuliana Pérez-Gallardo, Rubén Posada-Gómez
1Division of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, Mexico. galor@itorizaba.edu.mx
Informatics for Health & Social Care
|June 5, 2012
Summary
The iPixel Visual Search Engine improves medical image retrieval by combining semantic keywords and visual features for digitized mammograms. This content-based image retrieval (CBIR) system aids radiologists in diagnosing breast diseases.
Area of Science:
- Medical Imaging
- Computer Science
- Artificial Intelligence
Background:
- Traditional search engines struggle with image retrieval due to limitations in semantic understanding and feature-based querying.
- Content-based image retrieval (CBIR) systems are crucial in specialized domains like healthcare for analyzing visual medical data.
- Digitized mammograms contain vital information for breast disease diagnosis, necessitating improved retrieval methods.
Purpose of the Study:
- To introduce the iPixel Visual Search Engine, a novel system for searching digitized mammograms.
- To address the limitations of traditional search engines by integrating semantic and visual content analysis.
- To enhance the diagnostic capabilities of medical professionals through advanced image retrieval.
Main Methods:
- Development of the iPixel Visual Search Engine, incorporating collective intelligence and a CBIR algorithm.
- Implementation of search functionalities that compare semantic meanings and visual features of mammograms.
- Analysis of image features including the number, size (maximum and minimum), and average intensity level of regions.
Main Results:
- The iPixel system enables retrieval of mammogram features by considering both semantic context and visual characteristics.
- Comparisons are performed across multiple visual parameters, offering a comprehensive analysis.
- The system successfully integrates collective intelligence with CBIR algorithms for enhanced search accuracy.
Conclusions:
- The iPixel Visual Search Engine provides a robust solution for searching digitized mammograms, addressing key limitations in current image retrieval.
- The system supports differential diagnoses of breast diseases by offering detailed feature retrieval.
- Validation by radiologists and digital image analysis experts confirms the system's utility and effectiveness in the healthcare domain.
