PET medical image retrieval using fuzzy connectedness image segmentation and geometric moments

2015 ◽  
Author(s):  
A. P. Bhagat ◽  
M. Atique
Keyword(s):  
Image Segmentation ◽  
Image Retrieval ◽  
Medical Image ◽  
Fuzzy Connectedness ◽  
Medical Image Retrieval ◽  
Geometric Moments

Medical Image Mining Using Fuzzy Connectedness Image Segmentation

2016 ◽  
pp. 184-209
Author(s):  
Amol P. Bhagat ◽  
Mohammad Atique
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Image Feature ◽  
Image Mining ◽  
Imaging Data ◽  
Dicom Image ◽  
Fuzzy Connectedness ◽  
Geometric Moments ◽  
Novel Approach ◽  
Minimum Number

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.

Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique

2021 ◽  
Vol 14 (2) ◽  
pp. 48-66
Author(s):  
Sneha Kugunavar ◽  
Prabhakar C. J.
Keyword(s):  
Image Retrieval ◽  
Delaunay Triangulation ◽  
Medical Image ◽  
Region Of Interest ◽  
Texture Features ◽  
Ct Images ◽  
Medical Image Retrieval ◽  
Segmentation Technique ◽  
Geometric Moments ◽  
Benchmark Datasets

This article presents a novel technique for retrieval of lung images from the collection of medical CT images. The proposed content-based medical image retrieval (CBMIR) technique uses an automated image segmentation technique called Delaunay triangulation (DT) in order to segment lung organ (region of interest) from the original medical image. The proposed method extracts novel and discriminant features from the segmented lung region instead of extracting novel features from the whole original image. For the extraction of shape features, the authors employ edge histogram descriptor (EHD) and geometric moments (GM), and for the extraction of texture features, the authors use gray-level co-occurrence matrix (GLCM) technique. The shape and texture features are combined to form the hybrid feature which is used for retrieval of similar lung images. The proposed method is evaluated using two benchmark datasets of lung CT images. The simulation results prove that the proposed CBMIR framework shows improved performance in terms of retrieval accuracy and retrieval time.

Medical Image Mining Using Fuzzy Connectedness Image Segmentation

Biometrics ◽  
2017 ◽  
pp. 233-258
Author(s):  
Amol P. Bhagat ◽  
Mohammad Atique
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Image Feature ◽  
Image Mining ◽  
Imaging Data ◽  
Dicom Image ◽  
Fuzzy Connectedness ◽  
Geometric Moments ◽  
Novel Approach ◽  
Minimum Number

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.

scholarly journals Deep triplet hashing network for case-based medical image retrieval

2021 ◽  
Vol 69 ◽  
pp. 101981
Author(s):  
Jiansheng Fang ◽  
Huazhu Fu ◽  
Jiang Liu
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Image Retrieval ◽  
Case Based
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