Content-Based Retrieval for Mammograms

Content-Based Retrieval for Mammograms

Chia-Hung Wei (Ching Yun University, Taiwan), Chang-Tsun Li (University of Warwick, UK) and Yue Li (University of Warwick, UK)
DOI: 10.4018/978-1-60566-174-2.ch014
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Abstract

As distributed mammogram databases at hospitals and breast screening centers are connected together through PACS, a mammogram retrieval system is needed to help medical professionals locate the mammograms they want to aid in medical diagnosis. This chapter presents a complete content-based mammogram retrieval system, seeking images that are pathologically similar to a given example. In the mammogram retrieval system, the pathological characteristics that have been defined in Breast Imaging Reporting and Data System (BI-RADSTM) are used as criteria to measure the similarity of the mammograms. A detailed description of those mammographic features is provided in this chapter. Since the user’s subjective perception should be taken into account in the image retrieval task, a relevance feedback function is also developed to learn individual users’ knowledge to improve the system performance.
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1. Introduction

In hospitals and medical institutes, a large number of medical images are being produced in ever increasing quantities and used for diagnostics and therapy. The need for effective methods to manage and retrieve those image resources has been actively pursued in the medical community. The design of Picture Archiving and Communication Systems (PACS) is to integrate imaging modalities and interfaces with hospital and departmental information systems in order to manage the storage and distribution of images to radiologists, physicians, specialists, clinics, and imaging centers (Huang, 2003). A crucial requirement in PACS is to provide an efficient search function to access desired images. Image search in the digital imaging and communication in medicine (DICOM) protocol of PACS is currently carried out according to the alphanumerical order of the textual attributes of images (Lehmann et al., 2003). However, the information which users are interested in is the visual content of medical images rather than that residing in alphanumerical format. Traditional search requires images to be annotated with text, allowing the images to be accessed by text-based retrieval. As the size of the medical image database grows, it becomes impractical to manually annotate all contents and attributes of the images. The content of images is an informative and direct query which can be used to search for other images containing similar content. As content-based access approaches are expected to have a great impact on PACS and health database management, content-based image retrieval has been proposed for inclusion in PACS (Lehmann et al., 2003). In a PACS environment, a content-based image search system can support the usual comparisons made on images by physicians, answering similarity queries over the images stored in the distributed databases (Muller et al., 2004). The importance of searching for similar images comes from the fact that physicians usually try to recall similar cases by seeking images that are pathologically similar to a given image (Traina et al., 2005). As medical images are digitally represented in a multitude of formats based on their modality and the scanning device used, image retrieval systems have to be developed for their specific image types. Although content-based image retrieval has frequently been proposed for use in medical image management, only a few content-based retrieval systems have been developed specifically for medical images. These research-oriented systems are usually constructed in research institutes and continue to be improved, developed, and evaluated over time. Those systems include ASSERT for High-Resolution Computed Tomography (HRCT) of lungs (Shyu et al., 1999), CasImage for a variety of images from CT, MRI, and radiographs, to color photos (Muller et al., 2004), IRMA for various imaging modalities (Lehmann et al., 2004), and NHANES II for cervical and lumbar spine X-ray images (Antani et al., 2004).

Breast cancer is the most common cancer among women and affects approximately one million women worldwide. In the UK, for example, breast cancer accounts for 30 per cent of all female cancers and approximately 1 in 9 women in the UK will suffer from breast cancer sometime during their life (Beaver & Witham, 2007). Mammography has been a reliable method for the detection of breast cancer (Highnam & Brady, 1999) and women are usually asked to take mammograms regularly, and as a result many digital mammograms are produced in hospitals and breast screening centers. As distributed mammogram databases at hospitals and breast screening centers connect together through PACS, a mammogram retrieval system is needed to help medical professionals locate the mammograms they want in aid of medical diagnosis and case-based reasoning (Wei et al., 2006), thereby reducing false positives and false negatives in medical screening.

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Table of Contents
Acknowledgment
Zongmin Ma
Chapter 1
Danilo Avola, Fernando Ferri, Patrizia Grifoni
The novel technologies used in different application domains allow obtaining digital images with a high complex informative content, which can be... Sample PDF
Genetic Algorithms and Other Approaches in Image Feature Extraction and Representation
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Chapter 2
Dany Gebara, Reda Alhajj
This chapter presents a novel approach for content-fbased image retrieval and demonstrates its applicability on non-texture images. The process... Sample PDF
Improving Image Retrieval by Clustering
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Chapter 3
Gang Zhang, Z. M. Ma, Li Yan
Texture feature extraction and description is one of the important research contents in content-based medical image retrieval. The chapter first... Sample PDF
Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval
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Chapter 4
Jafar M. Ali
Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. Thus, it is necessary to develop... Sample PDF
Content-Based Image Classification and Retrieval: A Rule-Based System Using Rough Sets Framework
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Chapter 5
David García Pérez, Antonio Mosquera, Stefano Berretti, Alberto Del Bimbo
Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of... Sample PDF
Content Based Image Retrieval Using Active-Nets
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Chapter 6
Ming Zhang, Reda Alhajj
Content-Based Image Retrieval (CBIR) aims to search images that are perceptually similar to the querybased on visual content of the images without... Sample PDF
Content-Based Image Retrieval: From the Object Detection/Recognition Point of View
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Chapter 7
Chotirat “Ann” Ratanamahatana, Eamonn Keogh, Vit Niennattrakul
After the generation of multimedia data turning digital, an explosion of interest in their data storage, retrieval, and processing, has drastically... Sample PDF
Making Image Retrieval and Classification More Accurate Using Time Series and Learned Constraints
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Chapter 8
Hakim Hacid, Abdelkader Djamel Zighed
A multimedia index makes it possible to group data according to similarity criteria. Traditional index structures are based on trees and use the... Sample PDF
A Machine Learning-Based Model for Content-Based Image Retrieval
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Chapter 9
Ruofei Zhang, Zhongfei (Mark) Zhang
This chapter studies the user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem... Sample PDF
Solving the Small and Asymmetric Sampling Problem in the Context of Image Retrieval
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Chapter 10
Chia-Hung Wei, Chang-Tsun Li
An image is a symbolic representation; people interpret an image and associate semantics with it based on their subjective perceptions, which... Sample PDF
Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval
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Chapter 11
Pawel Rotter, Andrzej M.J. Skulimowski
In this chapter, we describe two new approaches to content-based image retrieval (CBIR) based on preference information provided by the user... Sample PDF
Preference Extraction in Image Retrieval
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Chapter 12
Iker Gondra
In content-based image retrieval (CBIR), a set of low-level features are extracted from an image to represent its visual content. Retrieval is... Sample PDF
Personalized Content-Based Image Retrieval
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Chapter 13
Zhiping Shi, Qingyong Li, Qing He, Zhongzhi Shi
Semantics-based retrieval is a trend of the Content-Based Multimedia Retrieval (CBMR). Typically, in multimedia databases, there exist two kinds of... Sample PDF
A Semantics Sensitive Framework of Organization and Retrieval for Multimedia Databases
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Chapter 14
Chia-Hung Wei, Chang-Tsun Li, Yue Li
As distributed mammogram databases at hospitals and breast screening centers are connected together through PACS, a mammogram retrieval system is... Sample PDF
Content-Based Retrieval for Mammograms
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Chapter 15
Ying-li Tian, Arun Hampapur, Lisa Brown, Rogerio Feris, Max Lu, Andrew Senior
Video surveillance automation is used in two key modes: watching for known threats in real-time and searching for events of interest after the fact.... Sample PDF
Event Detection, Query, and Retrieval for Video Surveillance
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Chapter 16
Min Chen, Shu-Ching Chen
This chapter introduces an advanced content-based image retrieval (CBIR) system, MMIR, where Markov model mediator (MMM) and multiple instance... Sample PDF
MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework
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About the Contributors