• Journal of Korea Multimedia Society
• /
• v.18 no.7
• /
• pp.806-817
• /
• 2015

The contrast limited adaptive histogram equalization(CLAHE) is an advanced method for the histogram equalization which is a common contrast enhancement technique. The CLAHE divides the image into sections, and applies the contrast limited histogram equalization for each section. X-ray images can be classified into three areas: skin, bone, and air area. In clinical application, the interest area is limited to the skin or bone area depending on the diagnosis region. The CLAHE could deteriorate X-ray image quality because the CLAHE enhances the area which doesn't need to be enhanced. In this paper, we propose a new method which automatically determines the clip limit of CLAHE's parameter to improve X-ray image quality using fuzzy logic. We introduce fuzzy logic which is possible to determine clip limit proportional to the interest of users. Experimental results show that the proposed method improve images according to the user's preference by focusing on the subject.

• Journal of information and communication convergence engineering
• /
• v.13 no.4
• /
• pp.264-269
• /
• 2015

The chest X-ray image cannot be focused in the same manner that optical lenses are and the resultant image generally tends to be slightly blurred. Therefore, the methods to improve the quality of chest X-ray image have been studied. In this paper, the inherent noises of the input images are suppressed by adding the Laplacian image to the original. First, the chest X-ray image using an Gaussian high pass filter and an optimal high frequency emphasis filter has shown improvements in the edges and contrast of flat areas. Second, using fuzzy logic_histogram equalization, each pixel of the chest X-ray image shows the normal distribution of intensities that are not overexposed. As a result, the proposed method has shown the enhanced edge and contrast of the images with the noise canceling effect.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.12a
• /
• pp.295-299
• /
• 2002

This paper presents an image contrast enhancement technique for improving low contrast images. We applied fuzzy logic to develop an image contrast enhancement technique in the viewpoint of considering that the low pictorial information of a low contrast image is due to the vaguness or fuzziness of the multivalued levels of brightness rather than randomness. The fuzzy image contrast enhancement technique consists of three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. In the stage of image fuzzification, we need to select a crossover point. To select the crossover point automatically the K-means algorithm is used. The problem of crossover point selection can be considered as the two-category, object and background, classification problem. The proposed method is applied to an experimental image with 256 gray levels and the result of the proposed method is compared with that of the histogram equalization technique. We used the index of fuzziness as a measure of image quality. The result shows that the proposed method is better than the histogram equalization technique.

• Journal of information and communication convergence engineering
• /
• v.15 no.2
• /
• pp.131-136
• /
• 2017

Chest X-ray image cannot be focused in the same manner as optical lenses and the resultant image generally tends to be slightly blurred. Therefore, appropriate methods to improve the quality of chest X-ray image have been studied in this paper. As the frequency domain filters work well for slight blurring and moderate levels of additive noises, we propose an algorithm that is particularly suitable for enhancing chest image. First, the chest image using Gaussian high pass filter and the optimal high frequency emphasis filter shows improvements in the edges and contrast of the flat areas. Second, as compared to using histogram equalization where each pixel of chest image is characterized by a loss of detail and much noises, in using fuzzy logic, each pixel of chest image shows the detail preservation and little noise.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.2
• /
• pp.155-160
• /
• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.15 no.1
• /
• pp.35-44
• /
• 2015

The main purpose of image enhancement is to improve certain characteristics of an image to improve its visual quality. This paper proposes a method for image contrast enhancement that can be applied to both medical and natural images. The proposed algorithm is designed to achieve contrast enhancement while also preserving the local image details. To achieve this, the proposed method combines local image contrast preserving dynamic range compression and contrast limited adaptive histogram equalization (CLAHE). Global gain parameters for contrast enhancement are inadequate for preserving local image details. Therefore, in the proposed method, in order to preserve local image details, local contrast enhancement at any pixel position is performed based on the corresponding local gain parameter, which is calculated according to the current pixel neighborhood edge density. Different image quality measures are used for evaluating the performance of the proposed method. Experimental results show that the proposed method provides more information about the image details, which can help facilitate further image analysis.