Delaunay triangulation based image enhancement for echocardiography images

A novel image enhancement approach for automatic echocardiography image processing is proposed. The main steps include undecimated wavelet based speckle noise reduction, edge detection, followed by a regional enhancement process that employs Delaunay triangulation based thresholding. The edge detection is performed using a fuzzy logic based center point detection and a subsequent radial search based fuzzy multiscale edge detection. The edges obtained are used as the vertices for Delaunay triangulation for enhancement purposes. This method enhances the heart wall region in the echo image. This technique is applied to both synthetic and real image sets that were obtained from a local hospital

Regulating The Degree Of Contrast Enhancement In Global Histogram Equalization-Based Method For Grayscale Photo Processing

Global Histogram equalization (GHE) is a popular image contrast enhancement method. However, it is rarely used on photo processing because it tends to create noise-artifacts, especially in simple-structure-image. A few GHE-based methods have been proposed to address this issue but whether they are noise-artifacts-proof remains questionable. This is because the methods are fully automatic and the evaluation conducted was not comprehensive. A novel automatic GHE-based method called Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE) has been proposed in this thesis. It has been evaluated thoroughly together with the existing automatic methods. The results have proven that none of the automatic GHE-based methods is noise-artifacts-proof. The conclusion has motivated author to look into scalable GHE-based methods that allows user to regulate the degree of contrast enhancement. A novel scalable GHE-based method called Recursive Mean-Separate Histogram Equalization (RMSHE) has been proposed in this thesis. It has been evaluated thoroughly together with other two existing scalable methods - Clip Limited Adaptive HE (CLAHE) and Stark’s Adaptive HE (StarkAHE). The results of separate evaluations consistently showed that none of the three methods could effectively enhance the contrast of simple-structure-image without creating any noise-artifacts. Another novel scalable GHE-based method called Scalable Global Histogram Equalization with Selective Enhancement (SGHESE) has been developed then to overcome the limitation of the existing methods. Evaluation results showed that SGHESE could enhance the image’s contrast effectively without creating any noise-artifacts. The results of subjective evaluation involving human observer also showed that the preference level of SGHESE was significantly higher compared to those of other methods. Finally, the thesis recommends extending the study of SGHESE to color image processing because majority of the images nowadays are color images

Contrast enhancement for improved blood vessels retinal segmentation using top-hat transformation and otsu thresholding

Diabetic Retinopathy is a effect of diabetes. It results abnormalities in the retinal blood vessels. The abnormalities can cause blurry vision and blindness. Automatic retinal blood vessels segmentation on retinal image can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques. In image enhancement, it combined CLAHE with Top-hat transformation to improve image quality. The study used DRIVE dataset that provided retinal image data. The image data in dataset was generated by the fundus camera. The CLAHE and Top-hat transformation methods were applied to rise the contrast and reduce noise on the image. The images that had good quality could help the segmentation process to find blood vessels in retinal images appropriately by a computer. It improved the performance of the segmentation method for detecting blood vessels in retinal image. Otsu Thresholding was used to segment blood vessel pixels and other pixels as background by local threshold. To evaluation performance of the proposed method, the study has been measured accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the averages of accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively. It indicated that the proposed method was successful and well to work on blood vessels segmentation retinal images especially for thick blood vessels

The use of contextual techniques and textural analysis of satellite imagery in geological studies of arid regions

This Thesis examines the problem of extracting spatial information (context and texture) of use to the geologist, from satellite imagery. Part of the Arabian Shield was chosen to be the study area. Two new contextual techniques; (a) Ripping Membrane and (b) Rolling Ball were developed and examined in this study. Both new contextual based techniques proved to be excellent tools for visual detection and analysis of lineaments, and were clearly better than the 'traditional' spatial filtration technique. This study revealed structural lineaments, mostly mapped for the first time, which are clearly related to regional tectonic history of the area. Contextual techniques were used to perform image segmentation. Two different image segmentation methods were developed and examined in this study. These methods were the automatic watershed segmentation and ripping membrane/Laserscan system method (as this method was being used for the first time). The second method produced high accuracy results for four selected test sites. A new automatic lineament extraction method using the above contextual techniques was developed. The aim of the method was to produce an automatic lineament map and the azimuth direction of these lineaments in each rock type, as defined by the segmented regions. 75-85% of the visually traced lineaments were extracted by the automatic method. The automatic method appears to give a dominant trend slightly different (10° — 15°) from the visually determined trend. It was demonstrated that not all the different types of rock could be discriminated using the spectral image enhancement techniques (band ratio, principal components and decorrelation stretch). Therefore, the spatial grey level dependency matrix (SGLDM) was used to produce a texture feature image, which would enable distinctions to be made and overcome the limitations of spectral enhancement techniques. The SGLDM did not produce any useful texture features which can discriminate between every rock type in the selected test sites. It did, however, show some acceptable texture discrimination between some rock types. The remote sensing data examined in this thesis were the Landsat (multispectral scanner, Thematic Mapper), SPOT, and Shuttle Imaging Radar (SIR-B)

Image Forensics for Forgery Detection using Contrast Enhancement and 3D Lighting

Nowadays the digital image plays an important role in human life. Due to large growth in the image processing techniques, with the availability of image modification tools any modification in the images can be done. These modifications cannot be recognized by human eyes. So Identification of the image integrity is very important in today’s life. Contrast and brightness of digital images can be adjusted by contrast enhancement. Move and paste type of images are Created by malicious person, in which contrast of one source image is enhanced to match the other source image. Here in this topic contrast enhancement technique is used which aimed at detecting image tampering has grown in different applications area such as law enforcement, surveillance. Also with the contrast enhancement, we propose an improved 3D lighting environment estimation method based on a more general surface reflection model. 3D lighting environment is an important clue in an image that can be used for image forgery detection. We intend to employ fully automatic face morphing and alignment algorithms. Also we intend to use face detection method to detect the face existence and 3D lighting environment estimation to check originality of human faces in the image

Automatic image enhancement by artificial bee colony algorithm

editor: Zeng ZhuWith regard to the improvement of image quality, image enhancement is an important process to assist human with better perception. This paper presents an automatic image enhancement method based on Artificial Bee Colony (ABC) algorithm. In this method, ABC algorithm is applied to find the optimum parameters of a transformation function, which is used in the enhancement by utilizing the local and global information of the image. In order to solve the optimization problem by ABC algorithm, an objective criterion in terms of the entropy and edge information is introduced to measure the image quality to make the enhancement as an automatic process. Several images are utilized in experiments to make a comparison with other enhancement methods, which are genetic algorithm-based and particle swarm optimization algorithm-based image enhancement methods