New Contrast Enhancement Technique For Non-Uniform Illumination Digital Colour Medical Images

This dissertation presents a new non-linear contrast enhancement algorithm for non-uniform illumination and low contrast digital colour medical images. In this research study, medical microscopic cervical cell and human epithelial type 2 (HEp-2) cell images were employed as case studies. Commonly, the captured cell images from the video camera or digital camera have uneven illumination and poor contrast due to inadequate lighting, the quality of the image acquisition devices and/or environmental conditions. The problem of non-homogenous illumination is not considered by most of the developed contrast enhancement approaches when performing operations to improve the cell image quality. From the previous studies, although two non-linear dark and bright contrast enhancement methods were proposed, but each method was utilized to enhance the entire cell image. As a result, each resultant image contained only one enhanced region while degraded the contrast of another region extremely. Firstly, this proposed algorithm tackles the non-uniform illumination issue by implementing two modified Gaussian fuzzy membership functions to predetermined underexposed and overexposed regions. After obtaining more even illumination cell images, this proposed algorithm addresses the low contrast problem by proposing new non-linear dark region bright region contrast enhancement techniques to enhance dark and bright regions individually. Lastly, the enhanced pixels of each region are combined to form an enhanced image. According to the qualitative and quantitative analysis, the experimental results in greyscale and colour format showed that the proposed algorithm tends to provide clearer and informational enhanced images, least noise amplification, better differentiation between the cell and the background, better contrast and illumination, and capable to preserve the image naturalness as compared with other methods

Adaptive Local Fuzzy Based Region Determination Image Enhancement Techniques For Non-Uniform Illumination And Low Contrast Images

Local contrast enhancement is an approach to improve the local visibility detail of an image by increasing the contrast in local regions. Recently, researchers have shown an interest in solving the issue of non-uniform illumination. However, most of these techniques divide the image into two parts only namely over-exposed and under-exposed regions and try to enhance the poor contrast in both regions using same approach. However, these methods are not robust and they are specifically designed to solve a specific problem at one time. This limitation has motivated this study to propose a new technique to solve the abovementioned problems. In the beginning, Adaptive Local Exposure Based Region Determination (ALEBRD) method is proposed to determine and divide the image into three regions namely under-exposed, over-exposed, and well-exposed regions. The results show that the proposed ALEBRD method produced better region determination performance than the other state-of-the-art methods. Based on the qualitative analysis, it could determine those three regions with high accuracy. After that, contrast of each region will be enhanced using a new local contrast enhancement technique called Adaptive Fuzzy Exposure Local Contrast Enhancement (AFELCE). The proposed AFELCE method is specifically designed to enhance the contrast of each region using different approaches. The proposed AFELCE technique successfully improves the contrast of 300 low-contrast and non-uniform illumination images, taken from three different databases namely standard, underwater, and microscopic human sperm images. The proposed AFELCE method qualitatively and quantitatively outperforms the state-of-the-art methods,. Qualitatively, the proposed AFELCE method has successfully enhanced the contrast of those images by producing more uniform illumination images with high contrast. Quantitatively, the proposed AFELCE method produces the highest average of Entropy (E), Measure of Enhancement (EME) and Universal Image Quality Index (UIQI) for the standard image database with values of 7.582, 42.75 and 0.94 respectively. The similar results obtained for the underwater database images, where it produces the highest average of E, EME and UIQI values with 7.124, 41.13 and 0.89 respectivley. While for the microscopic human sperm image database, it produces the highest values for E and EME with values of 7.602 and 42.51 respectively, and . This study is suitable to be applied to a real time applications. Based on the good results obtained for standard, underwater, and microscopic human sperm images, the developed system has high potential and suitable to be applied to a real time applications

Enhancement Of The Low Contrast Image Using Fuzzy Set Theory

This paper presents a fuzzy grayscale enhancement technique for low contrast image. The degradation of the low contrast image is mainly caused by the inadequate lighting during image capturing and thus eventually resulted in nonuniform illumination in the image

Fuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images

Purpose – Pre-screening of skin lesion for malignancy is highly demanded as melanoma being a life-threatening skin cancer due to unpaired DNA damage. In this paper, lesion segmentation based on Fuzzy C-Means clustering using non-dermoscopic images has been proposed. Design/methodology/approach – The proposed methodology consists of automatic cluster selection for FCM using the histogram property. The system used the local maxima along with Euclidean distance to detect the binomial distribution property of the image histogram, to segment the melanoma from normal skin. As the Value channel of HSV color image provides better and distinct histogram distribution based on the entropy, it has been used for segmentation purpose. Findings – The proposed system can effectively segment the lesion region from the normal skin. The system provides a segmentation accuracy of 95.69 % and the comparative analysis has been performed with various segmentation methods. From the analysis, it has been observed that the proposed system can effectively segment the lesion region from normal skin automatically. Originality/Value – This paper suggests a new approach for skin lesion segmentation based on FCM with automatic cluster selection. Here, different color channel has also been analyzed using entropy to select the better channel for segmentation. In future, the classification of melanoma from benign naevi can be performed

Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization

Image enhancement aims at processing an input image so that the visual content of the output image is more pleasing or more useful for certain applications. Although histogram equalization is widely used in image enhancement due to its simplicity and effectiveness, it changes the mean brightness of the enhanced image and introduces a high level of noise and distortion. To address these problems, this paper proposes image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization (FIMHE). FIMHE uses fuzzy intensity measure to first segment the histogram of the original image, and then clip the histogram adaptively in order to prevent excessive image enhancement. Experiments on the Berkeley database and CVF-UGR-Image database show that FIMHE outperforms state-of-the-art histogram equalization based methods