Abstract
X-ray computed tomography (CT) is a powerful technique for non-destructive volumetric inspection of objects and is widely used for studying internal structures of a large variety of sample types. The raw data obtained through an X-ray CT practice is a gray-scale 3D array of voxels. This data must undergo a geometric feature extraction process before it can be used for interpretation purposes. Such feature extraction process is conventionally done manually, but with the ever-increasing trend of image data sizes and the interest in identifying more miniature features, automated feature extraction methods are sought. Given the fact that conventional computer-vision-based methods, which attempt to segment images into partitions using techniques such as thresholding, are often only useful for aiding the manual feature extraction process, machine-learning based algorithms are becoming popular to develop fully automated feature extraction processes. Nevertheless, the machine-learning algorithms require a huge pool of labeled data for proper training, which is often unavailable. We propose to address this shortage, through a data synthesis procedure. We will do so by fabricating miniature features, with known geometry, position and orientation on thin silicon wafer layers using a femtosecond laser machining system, followed by stacking these layers to construct a 3D object with internal features, and finally obtaining the X-ray CT image of the resulting 3D object. Given that the exact geometry, position and orientation of the fabricated features are known, the X-ray CT image is inherently labeled and is ready to be used for training the machine learning algorithms for automated feature extraction. Through several examples, we will showcase: (1) the capability of synthesizing features of arbitrary geometries and their corresponding labeled images; and (2) use of the synthesized data for training machine-learning based shape classifiers and features parameter extractors.
Highlights
Nondestructive volumetric analysis of samples, enabled by X-ray computed tomography (CT), has attracted scientists and engineers from a wide spectrum of disciplines that are interested in identification and measurement of miniature internal features of their samples [8]
This task is performed manually by the subject matter experts (SMEs). This faces several problems: (1) SMEs have a subjective perception of features. This fact impacts the achievable accuracy of measurements in the manual processes; (2) manual feature extraction is subject to error [19] [2]; and (3) with the trending growth of image data size and the interest in identifying more miniature features, the manual feature extraction practice is becoming increasingly tedious, if not impractical
A total of 2,430 features were synthesized for training AIbased feature extraction algorithms in the context of X-ray CT images
Summary
Nondestructive volumetric analysis of samples, enabled by X-ray computed tomography (CT), has attracted scientists and engineers from a wide spectrum of disciplines that are interested in identification and measurement of miniature internal features of their samples [8]. For proper interpretation of an X-ray CT image, one must be able to extract well-defined geometric features from the raw data, where the raw data is a gray-scale 3D array of voxels [27] This task is performed manually by the subject matter experts (SMEs). This faces several problems: (1) SMEs have a subjective perception of features This fact impacts the achievable accuracy of measurements in the manual processes; (2) manual feature extraction is subject to error [19] [2]; and (3) with the trending growth of image data size and the interest in identifying more miniature features, the manual feature extraction practice is becoming increasingly tedious, if not impractical. The most common approach for achieving this goal is use of computer-vison (CV) techniques, to segment the images into distinct partitions [7] [30] [20] [3] [29] [6]
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