Medical Image Mining Using Fuzzy Connectedness Image Segmentation: Efficient Retrieval of Patients' Stored Images

Medical Image Mining Using Fuzzy Connectedness Image Segmentation: Efficient Retrieval of Patients' Stored Images

Amol P. Bhagat (Sant Gadge Baba Amravati University, India) and Mohammad Atique (Sant Gadge Baba Amravati University, India)
DOI: 10.4018/978-1-4666-8811-7.ch009
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Abstract

This chapter presents novel approach fuzzy connectedness image segmentation with geometric moments (FCISGM) for digital imaging and communications in medicine (DICOM) image mining. As most of the medical imaging data is exchanged in DICOM format, this chapter focuses on the various methodologies available for DICOM image feature extraction and mining. The comparison of existing medical image mining approaches with the proposed FCISGM approach is provided in this chapter. After carrying out exhaustive results it has been found that proposed FCISGM method gives more precise results and requires minimum number of computations compare to other medical image mining approaches resulting in improved relevant outcomes.
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Background

The overview of some image retrieval or mining systems such as medical image retrieval system in oracle (Dimitrovski et al., 2009), medical image mining using pattern similarity scheme based on PANDA (Iakovidis et al., 2009) framework, etc. is provided in this section. This section describes the content based medical image mining and the need for content based medical image mining.

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