Performance Comparison of Feature Generation Algorithms for Mosaic Photoacoustic Microscopy

Thanh Dat Le,Seong Young Kwon,Changho Lee

doi:10.3390/photonics8090352

Thanh Dat Le, Seong Young Kwon + Show 1 more

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https://doi.org/10.3390/photonics8090352

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Abstract

Mosaic imaging is a computer vision process that is used for merging multiple overlapping imaging patches into a wide-field-of-view image. To achieve a wide-field-of-view photoacoustic microscopy (PAM) image, the limitations of the scan range of PAM require a merging process, such as marking the location of patches or merging overlapping areas between adjacent images. By using the mosaic imaging process, PAM shows a larger field view of targets and preserves the quality of the spatial resolution. As an essential process in mosaic imaging, various feature generation methods have been used to estimate pairs of image locations. In this study, various feature generation algorithms were applied and analyzed using a high-resolution mouse ear PAM image dataset to achieve and optimize a mosaic imaging process for wide-field PAM imaging. We compared the performance of traditional and deep learning feature generation algorithms by estimating the processing time, the number of matches, good matching ratio, and matching efficiency. The analytic results indicate the successful implementation of wide-field PAM images, realized by applying suitable methods to the mosaic PAM imaging process.

Highlights

Computer vision is a branch of artifact intelligence that simulates human interactions with algorithms that can solve recognition problems in the real world
The most significant limitation of computer vision for detecting the structure of an object is the correlation between the field of view (FOV) and resolution
New solutions are being investigated by applying deep learning algorithms for mosaic imaging, such as convolutional neural networks (CNNs) or GoodPoints [19]

Summary

Introduction

Computer vision is a branch of artifact intelligence that simulates human interactions with algorithms that can solve recognition problems in the real world. The most significant limitation of computer vision for detecting the structure of an object is the correlation between the field of view (FOV) and resolution. To achieve accurate mosaic imaging, image patches require overlapping parts for correlation comparison. The more extensively overlapping areas provide a greater chance of matching the patches together [4]. Mosaic imaging is widely applied in many fields such as astrophysics [5], automatic vehicles [6,7], agriculture [8,9], and biomedical imaging [10,11,12,13]

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