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The research presented in this article aims to determine the possibility of identifying macular pathologies from optical coherence tomography (OCT) images. Early detection of a pathology in the human eye retina is ben...
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Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation...
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In this paper we have addressed the implementation of the accumulation and projection of high-resolution event data stream (HD - 1280×720 pixels) onto the image plane in FPGA devices. The results confirm the feas...
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In the paper the problem of modeling of thermal processes in microcontroller system is addressed. The proposed models allow to describe thermal processes during work of an evaluation system. The temperature in critica...
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In the paper the numerical estimation of the internal positivity of the model of a two dimensional heat transfer process is addressed. The considered thermal process is described by the fractional order state equation...
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The aim of this article is to analyze the impact of adaptive histogram equalization of the human eye OCT B-scans on the effectiveness of the classification of pathological changes. Tests were performed on two datasets...
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ISBN:
(数字)9788362065486
ISBN:
(纸本)9798350373806
The aim of this article is to analyze the impact of adaptive histogram equalization of the human eye OCT B-scans on the effectiveness of the classification of pathological changes. Tests were performed on two datasets. The first dataset contains 4 class B-scans (CNV, DME, DRUSEN, and NORMAL) taken with the use of Spectralis Heidelberg device, while the second dataset contains images from two devices (Spectralis Heidelberg OCT and Avanti Optovue OCT) and additionally contains the VMT (i.e., vitreomacular traction) class. The images were subjected to standard histogram equalization and contrast limited adaptive histogram equalization (CLAHE) with various parameters. The classification process was carried out using VGG16 artificial neural network. It can be observed that histogram equalization has no significant impact on improving classification performance.
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