Content-Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare

Herrmann, Albany E.; Estrela, Vania V.

doi:10.4018/978-1-4666-9978-6.ch039

Cited by 24 publications

(12 citation statements)

References 72 publications

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“…This study is a first step towards the realization of a fully automatic blood cells segmentation and classification subsystem of a computer-aided clinical platform that can deliver telemedical care for remote and hard to reach places [45] [46].…”

Section: Leukocytes Countsmentioning

confidence: 99%

“…In the future, Content-Based Image Retrieval (CBIR) systems can integrate and handle information about blood cells count and fuse it with other image modalities [45] [46]. It is worth considering that the development of low-cost mobile microscopic devices can significantly improve the successful deployment of computer vision-based medical diagnosis solutions.…”

Section: Leukocytes Countsmentioning

confidence: 99%

See 1 more Smart Citation

Proposal of a Medical Algorithm Based on the Application of Digital Image Processing and Visual Communication Techniques

Monteiro¹,

Iano²,

França³

2018

SET IJBE

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Blood tests help detect various types of diseases, where the erythrocytes count (RBC) and leukocytes (WBC) can use automatic and/or manual methodologies. The evaluation of RBC and WBC has a direct impact on the diagnosis of anemia, leukemia, viral, parasitic and viral infections. Given the importance and wide applicability of the Watershed Transform (WT) and Morphological Operations (MO), they can segment medical images to increase the efficiency and reliability of medical diagnostics. This study uses a WT-MO algorithm for segmentation, detection, and counting of blood cells that attend the criteria of efficiency and reliability. This methodology may be the first step in making blood tests more accessible to people from developing and underdeveloped countries. The WT-MO algorithm has been benchmarked using 30 microscopy images of a blood smear. The WT-MO algorithm results presented high accuracy (93%). The simulations of the algorithm executed in different hardware platforms presented average simulation and processing time with less than 3 seconds per sample. Therefore, the WT-MO based algorithm is accurate, reliable and a low-cost technique, which can be applied as a third methodology to perform the laboratory tests and to speed up further medical diagnostic.

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Section: Leukocytes Countsmentioning

confidence: 99%

Section: Leukocytes Countsmentioning

confidence: 99%

Proposal of a Medical Algorithm Based on the Application of Digital Image Processing and Visual Communication Techniques

Monteiro¹,

Iano²,

França³

2018

SET IJBE

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“…Recent advances in artificial intelligence (AI) and computer‐aided decision support methods have produced various efficient ways to allow for learning about skin problems . In particular, advances in machine learning have spurred novel retrieval algorithms and aroused interest in content‐based image retrieval (CBIR) techniques, where computer vision methods are applied to search for similar images to a “query” image based on the content of the image and visual clues such as color, shape, and pattern, from large databases . In the medical domain, CBIR is designed to assist with finding similar, labeled, medical images from a curated database.…”

Section: Introductionmentioning

confidence: 99%

Using content‐based image retrieval of dermoscopic images for interpretation and education: A pilot study

Sadeghi

Chilana

Yap

et al. 2019

Skin Research and Technology

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Recent advances in artificial intelligence (AI) and computer-aided decision support methods have produced various efficient ways to allow for learning about skin problems. 1 In particular, advances in machine learning have spurred novel retrieval algorithms and aroused interest in content-based image retrieval (CBIR) techniques, where computer vision methods are applied to search for similar images to a "query" image based on the content of the image and visual clues such as color, shape, and pattern, from large databases. 2 In the medical domain, CBIR is designed to assist with finding similar, labeled, medical images from a curated database. Within the dermatology context, CBIR can assist with diagnosis or education by comparing visually similar skin lesion images, 3 removing the difficulties that can arise when trying to describe images with words. Since the database and the algorithms for these systems are curated for a specific area or problem, users are less likely to encounter irrelevant images, one of the main problems with generic search engines. Despite the proposed benefits of modern CBIR systems, most CBIR-related research to date has focused on improving the accuracy of AI systems for diagnostic decisions 4,5 : we know little about the perceived utility and usability of CBIR systems for end users from a human-computer interaction (HCI) perspective. 6 In this paper, we describe a pilot study on how an interactive dermoscopic

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“…Content-based image retrieval (CBIR) plays a vital role in image retrieval. CBIR aims to use the visual content of a query image to search similar images from an image database, which is useful in many fields, such as remote diagnosis [1], face recognition [2], and online shopping [3]. Concerning the huge storage and the complicated maintenance cost, more and more image owners prefer to outsource huge image datasets and image search services to public clouds.…”

Section: Introductionmentioning

confidence: 99%

Toward Efficient Encrypted Image Retrieval in Cloud Environment

Huang

Zhang

2019

IEEE Access

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Outsourcing image search services to public clouds is an ever-increasing trend. However, directly outsourcing image datasets to untrusted clouds introduces privacy concerns. Several secure image retrieval schemes have been proposed recently. However, most of them require participation from image owners when building secure indexes, which wastes many computational resources of the image owners. Several schemes are proposed to solve this problem, but they suffer from low search accuracy on large datasets. In this paper, we propose the first secure image retrieval scheme that simultaneously solves these two problems. To obtain higher search accuracy, we extract image features via fine-tuned convolutional neural networks. Then, the image features are encrypted by using the secure k-Nearest Neighbor algorithm. To improve search speed and reduce the cost of image owners, we let cloud servers locally build a secure hierarchical index graph by using the encrypted image features. Besides, the secure index can be built and updated in parallel. We provide security analysis for the proposed scheme. Performance evaluations on the CIFAR-10 dataset show that the proposed scheme is practical. Moreover, compared with a recent scheme, our scheme can save more index construction time and cost of image owners when building secure indexes. INDEX TERMS Content-based image retrieval, encrypted image retrieval, navigable small world graph, secure index.

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Content-Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare

Abstract: Content-Based Image Retrieval (CBIR) locates, retrieves and displays images

Cited by 24 publications

References 72 publications

Proposal of a Medical Algorithm Based on the Application of Digital Image Processing and Visual Communication Techniques

Proposal of a Medical Algorithm Based on the Application of Digital Image Processing and Visual Communication Techniques

Using content‐based image retrieval of dermoscopic images for interpretation and education: A pilot study

Toward Efficient Encrypted Image Retrieval in Cloud Environment

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