Biomedical Informatics and Smart Healthcare
ISSN: 3068-5524 (Online)
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TY - JOUR AU - Diwakar, Manoj PY - 2026 DA - 2026/02/02 TI - Saliency Object Detection-Based Medical Image Fusion: Future Directions for Smart Healthcare Systems JO - Biomedical Informatics and Smart Healthcare T2 - Biomedical Informatics and Smart Healthcare JF - Biomedical Informatics and Smart Healthcare VL - 2 IS - 1 SP - 1 EP - 4 DO - 10.62762/BISH.2025.918243 UR - https://www.icck.org/article/abs/BISH.2025.918243 KW - medical image fusion KW - object detection KW - total variation KW - saliency features AB - Saliency Object Detection-Based Medical Image Fusion concentrates on the most prominent anatomical parts that naturally attract attention. The process begins with a two-step pipeline. First, it identifies the salient regions those structures most critical to a diagnostic decision. These highlighted areas are then fed into a guided-filtering framework. This work blends them with complementary information from a second imaging modality, such as CT and MRI. The method uses total variation regularization to suppress noise while preserving edges. A saliency-based weighting scheme ensures that every key detail is retained. The result is a single, high-quality image that carries the full diagnostic power of both inputs, providing physicians with a clearer, more informative view without sacrificing clinically relevant information. SN - 3068-5524 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Diwakar2026Saliency,
author = {Manoj Diwakar},
title = {Saliency Object Detection-Based Medical Image Fusion: Future Directions for Smart Healthcare Systems},
journal = {Biomedical Informatics and Smart Healthcare},
year = {2026},
volume = {2},
number = {1},
pages = {1-4},
doi = {10.62762/BISH.2025.918243},
url = {https://www.icck.org/article/abs/BISH.2025.918243},
abstract = {Saliency Object Detection-Based Medical Image Fusion concentrates on the most prominent anatomical parts that naturally attract attention. The process begins with a two-step pipeline. First, it identifies the salient regions those structures most critical to a diagnostic decision. These highlighted areas are then fed into a guided-filtering framework. This work blends them with complementary information from a second imaging modality, such as CT and MRI. The method uses total variation regularization to suppress noise while preserving edges. A saliency-based weighting scheme ensures that every key detail is retained. The result is a single, high-quality image that carries the full diagnostic power of both inputs, providing physicians with a clearer, more informative view without sacrificing clinically relevant information.},
keywords = {medical image fusion, object detection, total variation, saliency features},
issn = {3068-5524},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 
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