A hybrid multi-panel image segmentation framework for improved medical image retrieval system
Đã lưu trong:
| Xuất bản năm: | PLoS One vol. 20, no. 2 (Feb 2025), p. e0315823 |
|---|---|
| Tác giả chính: | |
| Tác giả khác: | , , , , , , |
| Được phát hành: |
Public Library of Science
|
| Những chủ đề: | |
| Truy cập trực tuyến: | Citation/Abstract Full Text Full Text - PDF |
| Các nhãn: |
Không có thẻ, Là người đầu tiên thẻ bản ghi này!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3169105316 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0315823 |2 doi | |
| 035 | |a 3169105316 | ||
| 045 | 2 | |b d20250201 |b d20250228 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Faqir Gul | |
| 245 | 1 | |a A hybrid multi-panel image segmentation framework for improved medical image retrieval system | |
| 260 | |b Public Library of Science |c Feb 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Multi-panel images play an essential role in medical diagnostics and represent approximately 50% of the medical literature. These images serve as important tools for physicians to align various medical data (e.g., X-rays, MRIs, CT scans) of a patient into a consolidated image. This consolidated multi-panel image, represented by its component sub-images, contributes to a thorough representation of the patient’s case during diagnosis. However, extracting sub-images from the multi-panel images poses significant challenges for medical image retrieval systems, especially when dealing with regular and irregular image layouts. To address these challenges, this paper presents a novel hybrid framework that significantly enhances sub-image retrieval. The framework classifies medical images, employs advanced computer vision and image processing techniques including image projection profiles and morphological operations, and performs efficient segmentation of various multi-panel image types including regular and irregular medical images. The hybrid approach ensures accurate indexing and facilitates fast retrieval of sub-images by medical image retrieval systems. To validate the proposed framework, experiments were conducted on a set of medical images from publicly available datasets, including ImageCLEFmed 2013 to ImageCLEFmed 2016. The results show better performance compared to other methods, attaining an accuracy of 90.50% in image type identification and 91% and 92% in regular and irregular multi-panel image segmentation tasks, respectively. By achieving accurate and efficient segmentation across diverse multi-panel image types, our framework demonstrates significant potential to improve the performance of medical image retrieval systems. | |
| 653 | |a Performance enhancement | ||
| 653 | |a Medical databases | ||
| 653 | |a Law enforcement | ||
| 653 | |a Image segmentation | ||
| 653 | |a Computed tomography | ||
| 653 | |a Image retrieval | ||
| 653 | |a Medical imaging | ||
| 653 | |a Medical research | ||
| 653 | |a Retrieval | ||
| 653 | |a Image processing | ||
| 653 | |a Computer vision | ||
| 653 | |a Statistical methods | ||
| 653 | |a Cultural heritage | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Shah, Mohsin | |
| 700 | 1 | |a Mushtaq, Ali | |
| 700 | 1 | |a Lal Hussain | |
| 700 | 1 | |a Sadiq, Touseef | |
| 700 | 1 | |a Adeel Ahmed Abbasi | |
| 700 | 1 | |a Mohammad Shahbaz Khan | |
| 700 | 1 | |a Alkahtani, Badr S | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 2 (Feb 2025), p. e0315823 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3169105316/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3169105316/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3169105316/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |