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

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Author(s): Amol P. Bhagat (Sant Gadge Baba Amravati University, India)and Mohammad Atique (Sant Gadge Baba Amravati University, India)
Copyright: 2016
Pages: 26
Source title: Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes
Source Author(s)/Editor(s): Wahiba Ben Abdessalem Karâa (Taif University, Saudi Arabia & RIADI-GDL Laboratory, ENSI, Tunisia)and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India)
DOI: 10.4018/978-1-4666-8811-7.ch009

Keywords: Bioinformatics / Medical Information Science Reference / Medicine & Healthcare

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

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