Biomedical Image Retrieval Using Multimodal Context and Concept Feature Spaces

2012 ◽  
pp. 24-35 ◽  
Author(s):  
Md. Mahmudur Rahman ◽  
Sameer K. Antani ◽  
Dina Demner Fushman ◽  
George R. Thoma
Keyword(s):  
Image Retrieval ◽  
Biomedical Image ◽  
Feature Spaces ◽  
Biomedical Image Retrieval

Most Significant Bit-Plane Based Local Ternary Pattern for Biomedical Image Retrieval

2021 ◽  
pp. 193-203
Author(s):  
Nilima Mohite ◽  
Manisha Patil ◽  
Anil Gonde ◽  
Laxman Waghmare
Keyword(s):  
Image Retrieval ◽  
Biomedical Image ◽  
Local Ternary Pattern ◽  
Biomedical Image Retrieval ◽  
Bit Plane

An efficient and robust approach for biomedical image retrieval using Zernike moments

2018 ◽  
Vol 39 ◽  
pp. 459-473 ◽  
Author(s):  
Yogesh Kumar ◽  
Ashutosh Aggarwal ◽  
Shailendra Tiwari ◽  
Karamjeet Singh
Keyword(s):  
Image Retrieval ◽  
Zernike Moments ◽  
Robust Approach ◽  
Biomedical Image ◽  
Biomedical Image Retrieval

scholarly journals Evaluating performance of biomedical image retrieval systems—An overview of the medical image retrieval task at ImageCLEF 2004–2013

2015 ◽  
Vol 39 ◽  
pp. 55-61 ◽  
Author(s):  
Jayashree Kalpathy-Cramer ◽  
Alba García Seco de Herrera ◽  
Dina Demner-Fushman ◽  
Sameer Antani ◽  
Steven Bedrick ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Retrieval Task ◽  
Medical Image Retrieval ◽  
Biomedical Image ◽  
Retrieval Systems ◽  
Biomedical Image Retrieval

scholarly journals Local bit-plane neighbour dissimilarity pattern in non-subsampled shearlet transform domain for bio-medical image retrieval

2021 ◽  
Vol 19 (2) ◽  
pp. 1609-1632
Author(s):  
Hilly Gohain Baruah ◽  
◽  
Vijay Kumar Nath ◽  
Deepika Hazarika ◽  
Rakcinpha Hatibaruah
Keyword(s):  
Image Retrieval ◽  
Input Image ◽  
Directional Sensitivity ◽  
Local Energy ◽  
Translational Invariance ◽  
Shearlet Transform ◽  
Biomedical Image ◽  
Biomedical Image Retrieval ◽  
Bit Plane ◽  
Magnetic Resonance Imaging Mri

<abstract><p>This paper introduces a novel descriptor non-subsampled shearlet transform (NSST) local bit-plane neighbour dissimilarity pattern (NSST-LBNDP) for biomedical image retrieval based on NSST, bit-plane slicing and local pattern based features. In NSST-LBNDP, the input image is first decomposed by NSST, followed by introduction of non-linearity on the NSST coefficients by computing local energy features. The local energy features are next normalized into 8-bit values. The multiscale NSST is used to provide translational invariance and has flexible directional sensitivity to catch more anisotropic information of an image. The normalised NSST subband features are next decomposed into bit-plane slices in order to capture very fine to coarse subband details. Then each bit-plane slices of all the subbands are encoded by exploiting the dissimilarity relationship between each neighbouring pixel and its adjacent neighbours. Experiments on two computed tomography (CT) and one magnetic resonance imaging (MRI) image datasets confirms the superior results of NSST-LBNDP when compared to many recent well known relevant descriptors both in terms of average retrieval precision (ARP) and average retrieval recall (ARR).</p></abstract>

A concept-based interactive biomedical image retrieval approach using visualness and spatial information

2015 ◽  
Author(s):  
Md M. Rahman ◽  
Sameer K. Antani ◽  
Dina Demner-Fushman ◽  
George R. Thoma
Keyword(s):  
Image Retrieval ◽  
Spatial Information ◽  
Biomedical Image ◽  
Biomedical Image Retrieval
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