Prof. Vincent Tam Profile

Prof. Vincent Tam

at Univ of Hong Kong

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 13 March 2024 Poster + Paper

Monitoring the progress of subconjunctival hemorrhage through spectral reconstruction from RGB images

Yuxing Li, Pak Wing Chiu, Yanmin Zhu, Yuqing Cao, Vincent Tam, Allie Lee, Edmund Lam

Proceedings Volume 12857, 128570F (2024) https://doi.org/10.1117/12.3000905

KEYWORDS: RGB color model, Eye, Image segmentation, Hyperspectral imaging, Color, Image restoration, Medicine, Eye models, Spectroscopy, Blood, Medical image reconstruction

Read Abstract +

Subconjunctival hemorrhage (SCH) is a prevalent ocular condition characterized by the accumulation of blood beneath the conjunctiva, resulting in a visible red patch on the eye’s surface. The appearance of SCH and the limited understanding of its progression can cause significant anxiety for patients. To address this issue and enhance ocular health management, we develop a deep learning-enabled monitoring approach that quantitatively tracks the SCH healing process through spectral reconstruction. Our approach comprises two key technical components. Firstly, automatic white balance algorithms are employed to estimate the light source’s color temperature and adjust image colors, minimizing the impact of varying lighting conditions. The SCH segmentation achieves an accuracy of 96.2 %, effectively avoiding interference from skin and eyelashes. Secondly, our monitoring approach evaluates SCH color changes, which are crucial for determining the stage of recovery. By learning a complex mapping function, the approach generates 31 hyperspectral bands (400–700 nm) by recovering the lost spectral information from a given RGB image. This process allows for a more detailed spectroscopic assessment of the affected area. The rich spectral signatures obtained from these hyperspectral images enable the classification of SCH into three distinct stages, reflecting the blood reabsorption process. This study is the first to apply deep learning-based spectral reconstruction to SCH determination, enabling evaluation of the recovery process through spectroscopic and quantitative analysis. This approach has the potential to improve daily patient care and promote better eye health control by offering more comprehensive monitoring of SCH progression.

Proceedings Article | 13 March 2024 Presentation + Paper

An intelligent and handheld device for early identification of meibomian gland irregularities

Yuxing Li, Hok Shing Kan, Yanmin Zhu, Yuqing Cao, Vincent Tam, Allie Lee, Edmund Lam

Proceedings Volume 12824, 128240D (2024) https://doi.org/10.1117/12.2692131

KEYWORDS: Eye, Infrared imaging, Diseases and disorders, Infrared cameras, Portability, Image segmentation, Eye models, Deep learning, Digital image processing

Read Abstract +

Meibomian gland dysfunction (MGD) is a significant cause of evaporative dry eye disease, occurring when the meibomian glands (MGs) in the eyelids produce abnormal lipid amounts. MG morphological features are crucial indicators of MG function and dry eye symptoms. However, the relationship between MG morphological irregularities and MGD remains unclear. To address this, we develop an integrated deep-learning-enabled monitoring system within a portable meibography device, enabling early identification and quantification of irregularly-shaped MGs. Our approach comprises two key technical components. First, a customized model is fine-tuned to classify MG irregularities into four types: overlapping, shortening, thickening, and tortuosity. We then quantitatively analyze MG irregularity ratios among four meiboscore groups of varying MG atrophy degrees and examine their connection to Ocular Surface Disease Index (OSDI) indexes from a subjective symptom perspective. From meiboscore 0 to 3, the overlapping MG ratio decreases by 17 %, and the shortening MG ratio increases by 12 %. Furthermore, we’ve built a handheld device equipped with infrared (IR) LED arrays and a USB camera to facilitate long-term and dynamic assessment. This meibography technology is compatible with common operating systems and can be integrated into a smartphone. The high-resolution images captured by this device can be used to assess various types of irregularities. This intelligent portable system offers an automatic and efficient quantitative evaluation of MG morphological irregularities, enabling home inspection and reducing costs. It has the potential to be applied in diagnosing and monitoring MG conditions, facilitating the management of MGD.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years