An improved gray-scale transformation method for pseudo-color image enhancement

Gao, Haiying; Zeng, Wen; Chen, J.

doi:10.18287/2412-6179-2019-43-1-78-82

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…The solution is obtained by minimizing the sum of the residual functional

and the stabilizer. Due to the practical importance of such methods, their development remains relevant [ 2 , 23 , 24 , 25 , 26 ]. However, the regularized least squares functional is not, in general, related to estimation error.…”

Section: Methodsmentioning

confidence: 99%

Measurement Reduction Methods for Processing Tomographic Images

Chulichkov¹,

Балакин²

2023

Sensors

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The importance of development of new methods for reconstruction of an object image given its sinogram and some additional information about the object stems from the possibility of artifact presence in the reconstructed image, or its insufficient sharpness when the used additional information does not hold. The problem of recovering artifact-free images of the studied object from tomography data is considered in the framework of the theory of computer-aided measuring systems. Methods for solving it are developed. They are based on narrowing the class of possible images using less artifact-inducing information. An example of such information is the natural condition of non-negativeness of the estimated brightnesses. The main problem that arises is the large dimensionality of the images, which prevents the use of direct algorithms. One proposed method is based on local approach, namely correction of the result of unfiltered backprojection by applying a locally (in the space of the output image) optimal linear transformation. Another method processes a sinogram directly, without using backprojection, using iterative implementation of the measurement reduction technique. Examples of use of the proposed methods for processing teeth sinograms are given.

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“…The solution is obtained by minimizing the sum of the residual functional

Section: Methodsmentioning

confidence: 99%

Measurement Reduction Methods for Processing Tomographic Images

Chulichkov¹,

Балакин²

2023

Sensors

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“…In this work, the segmentation approach was explained in details in [18]. This approach exploits bilateral filtering [18][19], gray-level thresholding via a bit-plane slicing technique [18][19][20], bounding-box localization [21], and the maximum intensity projection algorithm [22].…”

Section: B Segmentation and Enhancement For Nodule Emphasismentioning

confidence: 99%

Accurate Quantification of Small Pulmonary Nodules Using 3D Reconstruction of 2D Computed Tomography Lung Images

Karrar

Mabrouk

Wahed

2023

Journal of Advanced Engineering Trends

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Lung cancer has a high incidence rate and is considered highly fatal because of its low survival rate at early stages compared to other cancers. Computed tomography (CT) scans can reveal pulmonary nodules of different shapes and volumes in two dimensional (2D) slices. Three-dimensional (3D) reconstruction of pulmonary nodules can assist the radiologist in early treatment appropriate for the 3D nodule volume screened. In this research, we present a 3D reconstruction algorithm that uses 2D CT slices to reconstruct a 3D lung nodule. The equivalent diameters of small nodules ranged from 3 to 30 mm. A segmentation approach (based on bounding boxes and maximum intensity projection) was applied. Extracting the lung nodules from the 2D candidate masses was performed via a rule-based classifier. Surface rendering was used to reconstruct 3D pulmonary nodules which were visualized on the 3D Slicer software. The 3D nodule volume, as well as the accuracy rate and error of volume estimation were calculated. The proposed methodology was validated against the actual volumes of 14 3D nodules from the Lung Image Database Consortium (LIDC) database. The proposed algorithm achieved a maximum accuracy of 99.6627 % for lung nodule volume estimation. The corresponding average accuracy rate and average percentage error were 97.34 % and 2.66 %, respectively. The screening of 3D lung nodules can support surgery planning via nodule volume estimation. The average accuracy and error rates of the 3D reconstruction algorithm showed promising results in comparison with other published studies.

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“…The more separated layers, the more information can be extracted to achieve the purpose of image enhancement. It is a kind of image enhancement technology with obvious visual effect and that is relatively less complicated [29].…”

Section: Pseudo-color Image Enhancementmentioning

confidence: 99%

Detection and recognition based on machine learning and deep learning for Morse signal in wide-band wireless spectrum

Gao

Zhang

et al. 2022

Preprint

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Morse code radio communication is one of the indispensable shortwave communication methods, but the automatic receiving effect is poor. The accuracy and speed of detection and recognition, in the environment of neighborhood interference and low signal-to-noise ratio (SNR), cannot meet the practical requirements, which is a problem that needs to be solved. Combined with machine learning and deep learning, we propose an automatic Morse signal detection and recognition algorithm based on the ED-FE-CCBC structure. First, based on the time-frequency spectrum, the radio audio signal is adaptively energy sorted, and the target signal is detected according to the characteristics of bandwidth, windowed standard deviation, etc., and its frequency points are calculated. Then, narrow-band digital filtering is performed on the original audio signal with this frequency point as the center frequency, the target signal frequency band is re-extracted on the time-frequency spectrum after noise reduction, and the frequency spectrum is enhanced by pseudo-color. Finally, the pseudo-color image is put into the proposed network for identification and to realize end-to-end decoding. To evaluate the algorithm’s effectiveness, we use two parts of the data of simulation signal and professional training terminal audio to conduct experiments. The results show that in a complex channel environment, the ED-FE-CCBC algorithm can effectively detect and recognize Morse signals. While ensuring real-time performance, the recognition accuracy is better than the most advanced methods.

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An improved gray-scale transformation method for pseudo-color image enhancement

Cited by 12 publications

References 9 publications

Measurement Reduction Methods for Processing Tomographic Images

Measurement Reduction Methods for Processing Tomographic Images

Accurate Quantification of Small Pulmonary Nodules Using 3D Reconstruction of 2D Computed Tomography Lung Images

Detection and recognition based on machine learning and deep learning for Morse signal in wide-band wireless spectrum

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