Ai Ping Yow

Epidermal segmentation in high-definition optical coherence tomography.

Epidermis segmentation is a crucial step in many dermatological applications. Recently, high-definition optical coherence tomography (HD-OCT) has been developed and applied to imaging subsurface skin tissues. In this paper, a novel epidermis segmentation method using HD-OCT is proposed in which the epidermis is segmented by 3 steps: the weighted least square-based pre-processing, the graph-based skin surface detection and the local integral projection-based dermal-epidermal junction detection respectively. Using a dataset of five 3D volumes, we found that this method correlates well with the conventional method of manually marking out the epidermis. This method can therefore serve to effectively and rapidly delineate the epidermis for study and clinical management of skin diseases.

Automatic retinal interest evaluation system (ARIES).

In recent years, there has been increasing interest in the use of automatic computer-based systems for the detection of eye diseases such as glaucoma, age-related macular degeneration and diabetic retinopathy. However, in practice, retinal image quality is a big concern as automatic systems without consideration of degraded image quality will likely generate unreliable results. In this paper, an automatic retinal image quality assessment system (ARIES) is introduced to assess both image quality of the whole image and focal regions of interest. ARIES achieves 99.54% accuracy in distinguishing fundus images from other types of images through a retinal image identification step in a dataset of 35342 images. The system employs high level image quality measures (HIQM) to perform image quality assessment, and achieves areas under curve (AUCs) of 0.958 and 0.987 for whole image and optic disk region respectively in a testing dataset of 370 images. ARIES acts as a form of automatic quality control which ensures good quality images are used for processing, and can also be used to alert operators of poor quality images at the time of acquisition.

Supervised 3D graph-based automated epidermal thickness estimation

Skin biopsies are frequently performed for the diagnosis of skin diseases. However, the invasive nature of biopsies confers many disadvantages. Non-invasive imaging using optical coherence tomography (OCT) with automated disease detection can help reduce requirements for biopsies. One of the features in skin diseases is abnormal epidermal thickness. Automated determination of epidermal thickness requires segmenting the epidermis and this paper presents a novel method of supervised graph-based epidermis segmentation. The cost function for the segmentation method is learned from manually marked images. Epidermis segmentation is followed by epidermal thickness estimation. Evaluation of the method on a dataset containing 10 OCT volumes gives promising results which demonstrate the utility of the proposed method for epidermis segmentation and epidermal thickness measurement.

Automated in vivo 3D high-definition optical coherence tomography skin analysis system

The in vivo assessment and visualization of skin structures can be performed through the use of high resolution optical coherence tomography imaging, also known as HD-OCT. However, the manual assessment of such images can be exhaustive and time consuming. In this paper, we present an analysis system to automatically identify and quantify the skin characteristics such as the topography of the surface of the skin and thickness of the epidermis in HD-OCT images. Comparison of this system with manual clinical measurements demonstrated its potential for automatic objective skin analysis and diseases diagnosis. To our knowledge, this is the first report of an automated system to process and analyse HD-OCT skin images.The in vivo assessment and visualization of skin structures can be performed through the use of high resolution optical coherence tomography imaging, also known as HD-OCT. However, the manual assessment of such images can be exhaustive and time consuming. In this paper, we present an analysis system to automatically identify and quantify the skin characteristics such as the topography of the surface of the skin and thickness of the epidermis in HD-OCT images. Comparison of this system with manual clinical measurements demonstrated its potential for automatic objective skin analysis and diseases diagnosis. To our knowledge, this is the first report of an automated system to process and analyse HD-OCT skin images.

Determining the difference in eyegaze measurements in individuals with Age related Macular Degeneration

Age-related Macular Degeneration (AMD) is one of the leading causes of blindness in the elderly. Visual loss associated with AMD often results in a central scotoma which is an alteration in the central vision, leading to distortion or loss of vision. Current methods of detecting AMD are typically manual, require holding fixation and an external response trigger. In this paper, we propose the use of eyegaze tracking to detect for the presence of AMD, using a simple set of test patterns. Experimental results show that the derived eyegaze measurements can help to identify individuals with AMD from healthy individuals. This could lead to the detection of AMD using eye tracking data, and could result in a potential system device for screening.Age-related Macular Degeneration (AMD) is one of the leading causes of blindness in the elderly. Visual loss associated with AMD often results in a central scotoma which is an alteration in the central vision, leading to distortion or loss of vision. Current methods of detecting AMD are typically manual, require holding fixation and an external response trigger. In this paper, we propose the use of eyegaze tracking to detect for the presence of AMD, using a simple set of test patterns. Experimental results show that the derived eyegaze measurements can help to identify individuals with AMD from healthy individuals. This could lead to the detection of AMD using eye tracking data, and could result in a potential system device for screening.

Skin surface topographic assessment using in vivo high-definition optical coherence tomography

Skin surface topography and wrinkles are important biophysical features in both dermatological and cosmeceutical practice and research. Current skin surface topography evaluation techniques, such as replica-based methods, have limitations which may result in errors and inaccurate measurement. High-Definition Optical Coherence Tomography (HD-OCT) is a recently-developed non-invasive skin imaging modality used clinically for visualizing skin structures. In this present work, we developed an automatic evaluation system to quantify the skin surface topography and wrinkles in HD-OCT images. Comparison of this system with subjective evaluations of skin surface roughness was carried out, and there was a good correlation between the results of these two methods of evaluation.

Three-dimensional graph-based skin layer segmentation in optical coherence tomography images for roughness estimation

Automatic skin layer segmentation in optical coherence tomography (OCT) images is important for a topographic assessment of skin or skin disease detection. However, existing methods cannot deal with the problem of shadowing in OCT images due to the presence of hair, scales, etc. In this work, we propose a method to segment the topmost layer of the skin (or the skin surface) using 3D graphs with a novel cost function to deal with shadowing in OCT images. 3D graph cuts use context information across B-scans when segmenting the skin surface, which improves the segmentation as compared to segmenting each B-scan separately. The proposed method reduces the segmentation error by more than 20% as compared to the best performing related work. The method has been applied to roughness estimation and shows a high correlation with a manual assessment. Promising results demonstrate the usefulness of the proposed method for skin layer segmentation and roughness estimation in both normal OCT images and OCT images with shadowing.

Detecting AMD caused vision scotoma through eye tracking

Age Related Macular Degeneration (AMD) is the third leading cause of blindness and the first one in the elderly. AMD usually causes vision loss as the central vision field, e.g., vision scotoma, blur, and distortion, et. al. In this paper, we propose to detect AMD caused vision scotoma through eye tracking. Compared with the current vision assessment methods, e.g., Amsler grid, Microperimetry and Preferential Hyperacuity Perimetry, the proposed method has several advantages. 1) It does not require the patient to stare at a fixed position throughout the test. 2) It does not require the patient to orally or manually report / mark out the vision impairment. 3) It is easy to operate thus a trained nurse is capable of operating the test. To implement the proposed method, we collected gaze data of 138 eyes of 75 patients, who are diagnosed as AMD patient by clinicians. Nidek Microperimetry is adopted as the gold standard to get the ground truth and to evaluate our method. The result verifies the effectiveness of detecting vision scotoma through eye tracking.

Automated basal cell carcinoma detection in high-definition optical coherence tomography

Basal cell carcinoma (BCC) is the most common non-melanoma skin cancer. Conventional diagnosis of BCC requires invasive biopsies. Recently, a high-definition optical coherence tomography (HD-OCT) technique has been developed, which provides a non-invasive in vivo imaging method of skin. Good agreements of BCC features between HD-OCT images and histopathological architecture have been found. Therefore it is possible to automatically detect BCC using HD-OCT. This paper presents a novel BCC detection method that consists of four steps: graph based skin surface segmentation, surface flattening, deep feature extraction and the BCC classification. The effectiveness of the proposed method is well demonstrated on a dataset of 5,040 images. It can therefore serve as an automatic tool for screening BCC.

AQUIR - a system to generate quantitative and customized vision measurement template

This paper presents a visual acuity measurement system, AQUIR, which provides the functionality to generate customized visual acuity charts quantitatively. Visual chart is the basic measure to evaluate the visual acuity. With low variability, the traditional visual chart is inflexible and the same chart is used repeatedly to conduct vision assessment. AQUIR system implements a real-time solution and it can customize the visual acuity test book with more flexibility. The process customizes the background, races and reading habits. The adjustability of font sizes and word sparsity subdivides the diagnostic result levels. Besides traditional printed text version, the codebook can be displayed on back-illuminated electronic devices. It widens the application areas for the AQUIR system and transforms the traditional style of the visual acuity measurement system. The dynamic visual acuity codebook will facilitate the operating procedures of visual acuity measurement and improve the accuracy of diagnosis. By breaking through traditional technology, the flexibility and accuracy of visual acuity measurement is improved and reproduction of the technology on multimedia can be performed.