Image retrieval by using histogram equalization and CBIR

Rachana, P. G.; Ranjitha, S.; Suresh, H. N.

doi:10.1109/iceeot.2016.7755325

Cited by 1 publication

(1 citation statement)

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“…In educational settings, an ideal clinical decision support system could provide images relevant to a clinical query for special cases, 19 as these images often convey essential information and can be very valuable for interpretation and education. In, 20 authors developed a retrieval system to assist clinicians in self-learning and differential diagnosis of lung cancer to reduce the inter-observer variability using the information of similar nodules.…”

Section: Learning About Dermatological Conceptsmentioning

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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Section: Learning About Dermatological Conceptsmentioning

confidence: 99%