Comparative analysis of image enhancement techniques for uterine fibroid ultrasound

Background: The Ultrasound image is a vital diagnostic tool in the preliminary clinical assessment of many diseases, especially in Obstetrics and Gynecology. However, poor ultrasound image quality often leads to the inaccurate diagnosis of diseases such as uterine fibroids. Many researchers have proposed various methods for improving ultrasound image quality. Objective: To explore by comparison of four image enhancement techniques, the best approach for the enhancement of uterine fibroid images towards achieving better diagnosis and proper management of the disease..Methodology: The study assessed and compared the performance of four (4) different image enhancement techniques namely; Contrast stretching, Gamma correction, Histogram equalization(HE) and Contrast limited adaptive histogram equalization (CLAHE) on uterine fibroid ultrasound image Twenty (20) Ultrasound images from thedatabasewere downloaded and processed in MATLAB (2015a version) using image processing toolbox. Based on histogram distribution and statistical features (Mean, Standard Deviation and Entropy), the enhanced images were evaluated and compared. Results: The results show that Contrast stretching performed better based on Histogram distribution while CLAHE shows superior performance on Statistical featuresConclusion: Contrast stretching and Contrast limited adaptive histogram equalization (CLAHE)have demostrated good performance in enhancement of uterine fibroid ultrasound ima

Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

International audienceBlood vessel images can provide considerable information of many diseases, which are widely used by ophthalmologists for disease diagnosis and surgical planning. In this paper, we propose a novel method for the blood Vessel Enhancement via Multi-dictionary and Sparse Coding (VE-MSC). In the proposed method, two dictionaries are utilized to gain the vascular structures and details, including the Representation Dictionary (RD) generated from the original vascular images and the Enhancement Dictionary (ED) extracted from the corresponding label images. The sparse coding technology is utilized to represent the original target vessel image with RD. After that, the enhanced target vessel image can be reconstructed using the obtained sparse coefficients and ED. The proposed method has been evaluated for the retinal vessel enhancement on the DRIVE and STARE databases. Experimental results indicate that the proposed method can not only effectively improve the image contrast but also enhance the retinal vascular structures and details

Automatic Detection of Vasculature from the Images of Human Retina Using CLAHE and Bitplane Decomposition

Retinal blood vessel detection and extraction is an essential step in understanding several eye related pathologies. It is the key in automatic screening systems for retinal abnormalities. We present a novel yet simple approach to the detection and segmentation of vasculature from the fundus images of the human retina. For the detection and extraction of blood vessels, the green channel of the image is separated. The green channel is preprocessed for a better contrast by using contrast limited adaptive histogram equalization (CLAHE) and mathematical morphology. On applying bitplane decomposition, bitplane 2 is found to carry important information on the topology of retinal vasculature. A series of morphological operations on bitplane 2 segment the vasculature accurately. The proposed algorithm is computationally simple and does not require a prior knowledge of other retinal features like optic disc and macula. The algorithm has been evaluated on a subset of MESSIDOR and DRIVE image databases with various visual qualities. Robustness with respect to changes in the parameters of the algorithm has been examined.

Comparative Analysis of Image Enhancement Techniques for Ultrasound Liver Image

Liver cancer is the sixth most common malignant tumour in the world and the third most common cause of cancer-related deaths worldwide. To diagnose such liver diseases, In this paper comparison has been made for various image enhancement techniques that are applied to liver ultrasound image. Three types of liver ultrasound images used are normal, benign and malignant liver images. The techniques, which are compared on the basis of two evaluation parameters Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) including, Contrast Stretching, Shock Filter, Histogram Equalization, Contrast Limited Adaptive Histogram Equalization (CLAHE).Such a comparison would be helpful in determining the best suited method for clinical diagnosis. It also has been observed that the Shock filter gives the better performance than others for liver ultrasonic image analysis.DOI:http://dx.doi.org/10.11591/ijece.v2i6.151

A Global Two-Stage Histogram Equalization Method for Gray-Level Images

Digital image histogram equalization is an important technique in image processing to improve the quality of the visual appearance of images. However, the available methods suffer from several problems such as side effects and noise, brightness and contrast problems, loss of information and details, and failure in enhancement and in achieving the desired results. Therefore, the Adaptive Global Two-Stage Histogram Equalization (GTSHE) method for visual property enhancement of gray-level images is proposed. The first stage aims to clip the histogram and equalize the clipped histogram based on the number of occurrences of gray-level values. The second stage adaptively adjusts the space between occurrences by using a probability density function and different cumulative distribution functions that depend on the available and missing gray-level occurrences. Experiments were conducted using a number of benchmark datasets of images such as the Galaxies, Biomedical, Miscellaneous, Aerials, and Texture datasets. The results of the experiments were compared with a number of well-known methods, i.e. HE, AHEA, ESIHE, and MVSIHE, to evaluate the performance of the proposed method. The evaluation analysis showed that the proposed GTSHE method achieved a higher accuracy rate compared to the other methods