Development Of Contrast Enhancement Method For Digital Images

Photos captured in the dark environments, which have insufficient or uneven lighting conditions, might lead to low contrast images. The night images are looked dark and not clear as compared to day images. Image enhancement methods can be applied to improve the image quality. Histogram equalization (HE) method is a common image enhancement method. Although researchers had proposed many enhancement methods which including global and local histogram equalization, there are still some problems faced which include over enhancement, shift of mean brightness and loss of details. Hence, two image enhancement methods were developed by cascading exposure sub-image histogram equalization (ESIHE) and contrast limited adaptive histogram equalization (CLAHE) in different sequences. ESIHE is a global histogram equalization based method, while CLAHE is a local histogram equalization based method. Then, these two proposed methods were compared with existing HE based methods qualitatively and quantitatively. The qualitative assessment is visual assessment survey, while quantitative assessments are. noise standard deviation (NSD), image variance (IV), speckle index (SI) and contrast per pixel (CPP). Based on the assessments, the method that applied ESIHE then followed by CLAHE is able to enhance images better than the method applied CLAHE first and followed by ESIHE. The output image have a natural appearance, high contrast, and the details of image are clear

Unified adaptive framework for contrast enhancement of blood vessels

Information about blood vessel structures influences a lot of diseases in the medical realm. Therefore, for proper localization of blood vessels, its contrast should be enhanced properly. Since the blood vessels from all the medical angio-images have almost similar properties, a unified approach for the contrast enhancement of blood vessel structures is very useful. This paper aims to enhance the contrast of the blood vessels as well as the overall contrast of all the medical angio-images. In the proposed method, initially, the vessel probability map is extracted using hessian eigenanalysis. From the map, vessel edges and textures are derived and summed at every pixel location to frame a unique fractional differential function. The resulting fractional value from the function gives out the most optimal fractional order that can be adjusted to improve the contrast of blood vessels by convolving the image using Grunwald-Letnikov (G-L) fractional differential kernel. The vessel enhanced image is Gaussian fitted and contrast stretched to get overall contrast enhancement. This method of enhancement, when applied to medical angio-images such as the retinal fundus, Computerised Tomography (CT), Coronary Angiography (CA) and Digital Subtraction Angiography (DSA), has shown improved performance validated by the performance metrics

Low-Illumination Road Image Enhancement by Fusing Retinex Theory and Histogram Equalization

Low-illumination image enhancement can provide more information than the original image in low-light scenarios, e.g., nighttime driving. Traditional deep-learning-based image enhancement algorithms struggle to balance the performance between the overall illumination enhancement and local edge details, due to limitations of time and computational cost. This paper proposes a histogram equalization–multiscale Retinex combination approach (HE-MSR-COM) that aims at solving the blur edge problem of HE and the uncertainty in selecting parameters for image illumination enhancement in MSR. The enhanced illumination information is extracted from the low-frequency component in the HE-enhanced image, and the enhanced edge information is obtained from the high-frequency component in the MSR-enhanced image. By designing adaptive fusion weights of HE and MSR, the proposed method effectively combines enhanced illumination and edge information. The experimental results show that HE-MSR-COM improves the image quality by 23.95% and 10.6% in two datasets, respectively, compared with HE, contrast-limited adaptive histogram equalization (CLAHE), MSR, and gamma correction (GC)

Multi scale entropy based adaptive fuzzy contrast image enhancement for crowd images

Contrast enhancement is a very important issue in image processing, pattern recognition and computer vision. Fuzzy logic based techniques perform enhancement using more detailed information of grayness of an image. However, these methods do not perform well on images taken in uncontrolled environment which pose different challenges such as illumination variation, perspective distortion and viewpoint variation. In this paper, we have worked to devise a more robust image enhancement method using fuzzy logic. We propose a novel multi scale entropy based measurement performed using fuzzy logic image processing and utilize it to define and enhance the contrast. For this purpose, we present a mathematical formula to calculate contrast using an adaptive amplification constant. Our approach uses both the local and global entropy information. We have experimented our algorithm on images from Crowd Counting UCF dataset, which contains very dense crowds and complex texture that stands in line with the challenges targeted in this paper. The results show an improved quality than original dataset images and prove that our method enhances the images with a more dynamic ranged contrast as well as better visual results

High Quality 3D Shape Reconstruction via Digital Refocusing and Pupil Apodization in Multi-wavelength Holographic Interferometry.

Multi-wavelength holographic interferometry (MWHI) has good potential for evolving into a high quality 3D shape reconstruction technique. There are several remaining challenges, including 1) depth-of-field limitation, leading to axial dimension inaccuracy of out-of-focus objects; and 2) smearing from shiny smooth objects to their dark dull neighbors, generating fake measurements within the dark area. This research is motivated by the goal of developing an advanced optical metrology system that provides accurate 3D profiles for target object or objects of axial dimension larger than the depth-of-field, and for objects with dramatically different surface conditions. The idea of employing digital refocusing in MWHI has been proposed as a solution to the depth-of-field limitation. One the one hand, traditional single wavelength refocusing formula is revised to reduce sensitivity to wavelength error. Investigation over real example demonstrates promising accuracy and repeatability of reconstructed 3D profiles. On the other hand, a phase contrast based focus detection criterion is developed especially for MWHI, which overcomes the problem of phase unwrapping. The combination for these two innovations gives birth to a systematic strategy of acquiring high quality 3D profiles. Following the first phase contrast based focus detection step, interferometric distance measurement by MWHI is implemented as a next step to conduct relative focus detection with high accuracy. This strategy results in ±100mm 3D profile with micron level axial accuracy, which is not available in traditional extended focus image (EFI) solutions. Pupil apodization has been implemented to address the second challenge of smearing. The process of reflective rough surface inspection has been mathematically modeled, which explains the origin of stray light and the necessity of replacing hard-edged pupil with one of gradually attenuating transmission (apodization). Metrics to optimize pupil types and parameters have been chosen especially for MWHI. A Gaussian apodized pupil has been installed and tested. A reduction of smearing in measurement result has been experimentally demonstrated.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91461/1/xulium_1.pd

Amélioration de l'image et la segmentation (applications en imagerie médicale)

Avancement dans l'acquisition d'image et le progrès dans les méthodes de traitement d'image ont apporté les mathématiciens et les informaticiens dans les domaines qui sont d'une importance énorme pour les médecins et les biologistes. Le diagnostic précoce de maladies (comme la cécité, le cancer et les problèmes digestifs) ont été des domaines d'intérêt en médecine. Développement des équipements comme microscope bi-photonique à balayage laser et microscope de fluorescence par réflexion totale interne fournit déjà une bonne idée des caractéristiques très intéressantes sur l'objet observé. Cependant, certaines images ne sont pas appropriés pour extraire suffisamment d'informations sur de cette image. Les méthodes de traitement d'image ont été fournit un bon soutien à extraire des informations utiles sur les objets d'intérêt dans ces images biologiques. Rapide méthodes de calcul permettent l'analyse complète, dans un temps très court, d'une série d'images, offrant une assez bonne idée sur les caractéristiques souhaitées. La thèse porte sur l'application de ces méthodes dans trois séries d'images destinées à trois différents types de diagnostic ou d'inférence. Tout d'abord, Images de RP-muté rétine ont été traités pour la détection des cônes, où il n'y avait pas de bâtonnets présents. Le logiciel a été capable de détecter et de compter le nombre de cônes dans chaque image. Deuxièmement, un processus de gastrulation chez la drosophile a été étudié pour observer toute la mitose et les résultats étaient cohérents avec les recherches récentes. Enfin, une autre série d'images ont été traités où la source était une vidéo à partir d'un microscopie photonique à balayage laser. Dans cette vidéo, des objets d'intérêt sont des cellules biologiques. L'idée était de suivre les cellules si elles subissent une mitose. La position de la cellule, la dispersion spatiale et parfois le contour de la membrane cellulaire sont globalement les facteurs limitant la précision dans cette vidéo. Des méthodes appropriées d'amélioration de l'image et de segmentation ont été choisies pour développer une méthode de calcul pour observer cette mitose. L'intervention humaine peut être requise pour éliminer toute inférence fausse.Advancement in Image Acquisition Equipment and progress in Image Processing Methods have brought the mathematicians and computer scientists into areas which are of huge importance for physicians and biologists. Early diagnosis of diseases like blindness, cancer and digestive problems have been areas of interest in medicine. Development of Laser Photon Microscopy and other advanced equipment already provides a good idea of very interesting characteristics of the object being viewed. Still certain images are not suitable to extract sufficient information out of that image. Image Processing methods have been providing good support to provide useful information about the objects of interest in these biological images. Fast computational methods allow complete analysis, in a very short time, of a series of images, providing a reasonably good idea about the desired characteristics. The thesis covers application of these methods in 3 series of images intended for 3 different types of diagnosis or inference. Firstly, Images of RP-mutated retina were treated for detection of rods, where there were no cones present. The software was able to detect and count the number of cones in each frame. Secondly, a gastrulation process in drosophila was studied to observe any mitosis and results were consistent with recent research. Finally, another series of images were treated where biological cells were observed to undergo mitosis. The source was a video from a photon laser microscope. In this video, objects of interest were biological cells. The idea was to track the cells if they undergo mitosis. Cell position, spacing and sometimes contour of the cell membrane are broadly the factors limiting the accuracy in this video. Appropriate method of image enhancement and segmentation were chosen to develop a computational method to observe this mitosis. Cases where human intervention may be required have been proposed to eliminate any false inference.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Imaging of the Breast

Early detection of breast cancer combined with targeted therapy offers the best outcome for breast cancer patients. This volume deal with a wide range of new technical innovations for improving breast cancer detection, diagnosis and therapy. There is a special focus on improvements in mammographic image quality, image analysis, magnetic resonance imaging of the breast and molecular imaging. A chapter on targeted therapy explores the option of less radical postoperative therapy for women with early, screen-detected breast cancers

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device

CLOSE-RANGE AND SATELLITE REMOTE SENSING OF ALGAL BIOMASS IN THE IOWA GREAT LAKES

The utility of both close-range and satellite remote sensing for assessing inland water quality was examined in the Iowa Great Lakes. The water quality of this system is of considerable interest because of its status as an environmental, recreational, and therefore, economic resource. The broad range of optical conditions present in the lakes and the wealth of literature on the system make it an ideal environment for water quality remote sensing research. The goal of this research was to survey the water quality of the Iowa Great Lakes via remote sensing, evaluate different predictive algorithms, and map the distribution of algal biomass. In situ sampling was carried out on Spirit, West Okoboji, and East Okoboji lakes, concurrent with a SPOT-2 satellite overpass, August 13, 1997. A total of 26 sample sites were visited. Measurements included chlorophyll a, turbidity, vertical attenuation, hyperspectral radiance/reflectance, and GPS. Aerial photographs were taken later in order to illustrate the spatial characteristics of various aquatic features. Empirical relationships between chlorophyll and both close-range (hyperspectral) and satellite (broad-band) data, were evaluated using correlation and regression techniques. Results suggest that low chlorophyll concentrations are difficult to estimate, while moderate to high biomass levels can be accurately modeled using either closerange or satellite data. The near-infrared portion of the optical spectrum proved the single most useful spectral region for estimating chlorophyll concentration. Due to their ability to correct for nonalgal scattering, the NIR:Red ratio (r2 = 0.997) and the Baseline Sum (r2 = 0.998) algorithms, proved the most effective for estimating chlorophyll In hypereutrophic East Okoboji Lake. Chlorophyll a maps were developed by applying an algorithm based on nearinfrared band radiance magnitude (r2 = 0.833) to the SPOT imagery. The distribution of algal biomass is predominantly homogeneous in West Okoboji and Spirit lakes, but extremely patchy in Lake East Okoboji. This spatial heterogeneity of water quality constituents may be a significant source of error for monitoring programs based solely on point sampling. Advisor: Donald C. Rundquis