• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.32-42
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• 2022

Purpose: To evaluate the clinical benefit of 2D contrast-enhanced T2 fluid-attenuated inversion recovery (CE-T2 FLAIR) image for detecting leptomeningeal metastasis (LM) in the brain metastasis work-up for lung cancer. Materials and Methods: From June 2017 to July 2019, we collected all consecutive patients with lung cancer who underwent brain magnetic resonance image (MRI), including contrast-enhanced 3D fast spin echo T1 black-blood image (CE-T1WI) and CE-T2 FLAIR; we recruited clinico-radiologically suspected LM cases. Two independent readers analyzed the images for LM in three sessions: CE-T1WI, CE-T2 FLAIR, and their combination. Results: We recruited 526 patients with suspected lung cancer who underwent brain MRI; of these, we excluded 77 (insufficient image protocol, unclear pathology, different contrast media, poor image quality). Of the 449 patients, 34 were clinico-radiologically suspected to have LM; among them, 23 were diagnosed with true LM. The calculated detection performance of CE-T1WI, CE-T2 FLAIR, and combined analysis obtained from the 34 suspected LM were highest in the combined analysis (AUC: 0.80, 0.82, and 0.89, respectively). The inter-observer agreement was also the highest in the combined analysis (0.68, 0.72, and 0.86, respectively). In quantitative analyses, CNR of CE-T2 FLAIR was significantly higher than that of CE-T1WI (Wilcoxon signed rank test, P < 0.05). Conclusion: Adding CE-T2 FLAIR might provide better detection for LM in the brain-metastasis screening for lung cancer.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.239-242
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• 2004

This paper presents a novel contrast enhance algorithm for images displayed with bright environment light. This algorithm is designed to preserve local contrast based on the luminance ratio of the pixel to its local surround in attention. This algorithm improves image quality of projectors in a bright room.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.260-263
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• 2017

본 논문은 single image에서 측정한 빛 전달량 값과 local contrast 값을 사용하여 안개 량을 수치화하는 방법을 제안한다. 제안하는 방법은 빛 전달량 값을 사용하여 안개로 예측되는 지역을 추정하고, 추정된 안개 예측지역의 넓이와 해당 지역의 local contrast 크기의 범위를 사용하여 안개 정도를 수치화 한다. single image에서 측정 가능한 안개 의 물리적 특성들을 고려하였기 때문에 기존의 안개 검출 알고리즘들이 구분하지 못했던 영상들에서도 안개 량을 정확하게 측정하였다. 실제 빛의 산란 정도를 측정하는 감광 계수 측정계를 사용하여 측정한 안개 량과 제안하는 방법의 수치를 비교했을 때, 다양한 환경과 물체를 포함한 영상들에서 95%이상의 정확도로 안개 정도를 수치화 하였다. 또한 빛 전달량 추정 과정에서 local contrast 값을 추출하여 사용하기 때문에 기존의 빛 전달량을 측정하는 방법에서 복잡도를 거의 증가시키지 않는다.

• Journal of Integrative Natural Science
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• v.8 no.2
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• pp.141-144
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• 2015

As the visual quality of X-ray images is a critical reference for the accuracy of the clinical diagnosis, the methods to improve the quality of X-ray images have been investigated. Among many existing methods, using frequency domain filter is a very powerful method to improve the visual quality of images. In this paper, the inherent noises of the input images are suppressed by adding the Laplacian image to the subjected image. The medical X-ray images using the optimal high pass filter has shown improved edges. Further, the optimal high frequency emphasis filter has shown the improved contrast of flat areas by using the result image from the optimal high pass filter. Also the resulting images of the global contrast have improved by the histogram equalization. As a result, the proposed methods have shown enhanced contrast and edges of the images with noise canceling effect.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.1035-1038
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• 2007

In this paper, we propose a color image improvement method. The proposed algorithms are classified with the adaptive contrast stretching method for contrast enhancement and the adaptive saturation enhancement method for saturation enhancement. The adaptive contrast stretching method is to compensate a significant change of brightness while luminance is processed. The adaptive saturation enhancement method inhibits its saturation from de-saturation and oversaturation while chrominance is processed. The proposed algorithms are focused on a preference color processing in order to generate better image quality than the algorithms focused on a uniform color processing for human vision.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1233-1243
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• 2015

The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.698-704
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• 2013

The image process for image enhancement applies differently the same algorithm for each application. So, the optimized value for each application is required. In this paper, the X-ray medical image using a high-pass filter was improved edges. The result image was improved edge and the contrast of flat area using a constant multiplier and offset. Therefore, the high-frequency emphasis filter optimized for medical image is required. These optimized values are the gaussian high-pass filter, the distance of cutoff frequency=0.05 and offset=0.5. From the result of optimaized simulation, The proposed method has enhanced contrast and edge of the image in the contrast of existing mothods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.539-557
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• 2013

A novel multifocus image fusion algorithm based on NSCT is proposed in this paper. In order to not only attain the image focusing properties and more visual information in the fused image, but also sensitive to the human visual perception, a local multidirection variance (LEOV) fusion rule is proposed for lowpass subband coefficient. In order to introduce more visual saliency, a modified local contrast is defined. In addition, according to the feature of distribution of highpass subband coefficients, a direction vector is proposed to constrain the modified local contrast and construct the new fusion rule for highpass subband coefficients selection The NSCT is a flexible multiscale, multidirection, and shift-invariant tool for image decomposition, which can be implemented via the atrous algorithm. The proposed fusion algorithm based on NSCT not only can prevent artifacts and erroneous from introducing into the fused image, but also can eliminate 'block effect' and 'frequency aliasing' phenomenon. Experimental results show that the proposed method achieved better fusion results than wavelet-based and CT-based fusion method in contrast and clarity.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.104-111
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• 2010

This paper presents an adaptive image enhancement method using the spatial frequency property in the DCT(discrete cosine transform) compressed domain. The dc coefficients, the illumination components of image, are adjusted to compress the dynamic range of image, and the ac coefficients are modified to enhance the contrast by using the human visual system(HVS) and the spatial frequency property. The ac coefficients are separated into vertical direction, horizontal direction, and mixed spatial frequency components, and adaptively modified to minimize the block artifacts that possibly occur in the image enhancement. The proposed method using dynamic range compression and adaptive contrast enhancement shows the advanced performance without the block artifact compared with existing method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.467-472
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• 2005

The principal objective of enhancement methods is to process an image so that the output image is more suitable than the original image lot a specific application. Images taken in the night can be low-contrast images because of poor environments. In this paper, we compared the performance of Image Contrast Enhancement Technique Using Clustering Algorithm(ICECA) with those of color adjustment methods such as Histogram Equalization(HE), Brightness Preserving Bi-Histogram Equalization(BBHE), and the Multi-Scale Refiner(MSR). We compared these methods by applying the image enhancement methods to a set of diverse images.