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pixels) in the binary 2D map. When the vasculature pixel is transformed (u, v) coordinates on the input image correspond to the vasculature pixel’s coordinates on the reference image, the MPC is incremented by 1. MPC is assumed be maximized when the image pair is perfectly geometrically aligned by the transformation. The ideal case is that all zero pixels of the input image are mapped onto zero pixels of the reference image. The problem can be mathematically formulated as the maximization of the following objective function:

u,v ROI

where fmpc denotes the value of the mutual-pixel-count. Tx and Ty are the transformations for u and v coordinates of the input image. The ROI (region of interest) is the vasculature region where the MPC is calculated on.

Ideally, fmpc appears to contain a global maximum surrounded by the numerous local maxima. During the iteration, the reference image control point coordinates are fixed. Only input image control point coordinates are subject to adjustment. The coordinate adjustments are iteratively implemented until one of the following convergence criteria is reached:

(1)Predefined maximum number of loops is reached and

(2)The updated fmpc is smaller than ε, i.e.

where ε is a very small nonnegative threshold.

4.7. Conclusion

The new algorithm presented in this chapter, which consists of the adaptive exploratory algorithm for the control point detection and heuristic optimization fusion, is reliable and time efficient. The new approach has been applied on three ophthalmologic modalities of nonhuman primate eyes: angiogram, fundus, and oxygen saturation images. It has achieved an excellent result by giving the visualization of fundus or oxygen saturation image with a complete angiogram overlay.

By locking the multi-sensor images in one place, the algorithm allows ophthalmologists to match the same eye over time to get a sense of disease

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progress and pinpoint surgical tools to increase accuracy and speed of the surgery. Early detection can allow timely treatment to prevent further vision loss, and to prolong effective years of usable vision. Therefore, multi-sensor retinal image registration and fusion, as a novel method in ophthalmology, is able to assist ophthalmologists in performing retinal abnormality analysis and disorder early detection. The new algorithm can be easily expanded to human or animals’ 3D eye, brain, or body image feature extraction, registration, and fusion.

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Chapter 5

Automated Localization of Eye

and Cornea Using Snake and

Target-Tracing Function

Jen-Hong Tan , Ng, E.Y.K. , Rajendra Acharya†, U.

and Chee, C.‡

Thermography is a noninvasive thermal imaging technique that is widely utilized in the medical and engineering arenas. Infrared (IR) thermography captures IR radiation from the surface of an object and illustrates the corresponding surface temperature as a thermogram. This technique is capable of detecting subtle temperature changes in vascular tissue, and, hence, is important in the detection of ocular diseases.

Localizing the eye and cornea is difficult in an IR thermogram, as the boundary between the iris and sclera is not discernable in a thermal image. This work presents an algorithm that correctly localizes the eye and cornea in an IR thermogram using the snake algorithm and target-tracing function. The target-tracing function is minimized in this algorithm to determine the shape and location of the initial contour, which in latter produces the best localization. With this function, user does not need to determine the region where the snake algorithm should proceed to converge beforehand. The corneal center and radius are estimated from the snake points that have successfully localized the eye feature. In this work, 181 normal and 88

School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 50, Nanyang Avenue, Singapore 639798.

†Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.

‡Medical & Surgical Retinal Centre Department of Ophthalmology, National University Hospital, Singapore 119074.

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