QualityAdaptive sharpness enhancement and noise removal of a colour images based on the bilateral filtering

In this paper, we present the Adaptive Bilateral Filter (ABF) for sharpness enhancement and noise removal of a colour images. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms. Compared with an USM based sharpening method, the optimal unsharp mask (OUM), In terms of noise removal, ABF will outperform the bilateral filter and the OUM. ABF works well for both gray images and color images. Due to operation of sharpening of colour images along the edge slope tend to poseterize the image using ABF by pulling up or pulling down the colour images. The proposed method is effective at removing signal noise while enhancing the experimental results in perceptual quality both quantatively and qualitatively

Downsampling methods for medical datasets

Volume visualization software usually has to deal with datasets that are larger than the GPUs may hold. This is especially true in one of the most popular application scenarios: medical visualization. Typically, medical datasets are available for different personnel, but only radiologists have high-end systems that are able to cope with large data. For the rest of physicians, usually low-end systems are only available. As a result, most volume rendering packages downsample the data prior to uploading to the GPU. The most common approach consists in performing iterative subsampling along the longest axis, until the model fits inside the GPU memory. This causes important information loss that affects the final rendering. Some cleverer techniques may be developed to preserve the volumetric information. In this paper we explore the quality of different downsampling methods and present a new approach that produces smooth lower-resolution representations, yet still preserves small features that are prone to disappear with other approaches.Peer ReviewedPostprint (published version

Effectiveness of filtering methods in enhancing pulmonary carcinoma image quality: a comparative analysis

In recent years, information technology has vastly improved. The quality of the image has been degraded by noise, which defeats the purpose of the noisy images. The major purpose of this paper is to find out which filters provide a better outcome while preprocessing medical images using computer tomography scans. The purpose of this paper is to remove noise from any images, whether they are real-time datasets or online datasets. To enhance an image for preprocessing, we have compared various filters; these filters are already available, but the major purpose is to identify the best filter. We compared the different parameters to find the best and finally found that the modified bilateral filtering provided a better result. The noise has been removed by using a bilateral filter, and the image clarity has not changed when using this filter. We have discussed the advantages and drawbacks of each approach. The effectiveness of these filters is compared using the peak signal-to-noise ratio, structural similarity index, contrast-to-noise ratio, and mean square error. The proposed algorithm is tested on 5 sample lung images. The results show that the modified bilateral filter produces better results

A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

Segmentation of brain tumors in magnetic resonance imaging is a one of the most complex processes in medical image analysis because it requires a combination of data knowledge with domain knowledge to achieve highly results. Such that, the data knowledge refers to homogeneity, continuity, and anatomical texture. While the domain knowledge refers to shapes, location, and size of the tumor to be delineated. Due to recent advances in medical imaging technologies which produce a massive number of cross-sectional slices, this makes a manual segmentation process is a very intensive, time-consuming and prone to inconsistences. In this study, an automated method for recognizing and segmenting the pathological area in MRI scans has been developed. First the dataset has been pre-processed and prepared by implementing a set of algorithms to standardize all collected samples. A particle swarm optimization is utilized to find the core of pathological area within each MRI slice. Finally, an active contour without edge method is utilized to extract the pathological area in MRI scan. Results reported on the collected dataset includes 50 MRI scans of pathological patients that was provided by Iraqi Center for Research and Magnetic Resonance of Al Imamain Al-Kadhimain Medical City in Iraq. The achieved accuracy of the proposed method was 92% compared with manual delineation

Disparity-compensated view synthesis for s3D content correction

International audienceThe production of stereoscopic 3D HD content is considerably increasing and experience in 2-view acquisition is in progress. High quality material to the audience is required but not always ensured, and correction of the stereo views may be required. This is done via disparity-compensated view synthesis. A robust method has been developed dealing with these acquisition problems that introduce discomfort (e.g hyperdivergence and hyperconvergence...) as well as those ones that may disrupt the correction itself (vertical disparity, color difference between views...). The method has three phases: a preprocessing in order to correct the stereo images and estimate features (e.g. disparity range...) over the sequence. The second (main) phase proceeds then to disparity estimation and view synthesis. Dual disparity estimation based on robust block-matching, discontinuity-preserving filtering, consistency and occlusion handling has been developed. Accurate view synthesis is carried out through disparity compensation. Disparity assessment has been introduced in order to detect and quantify errors. A post-processing deals with these errors as a fallback mode. The paper focuses on disparity estimation and view synthesis of HD images. Quality assessment of synthesized views on a large set of HD video data has proved the effectiveness of our method

Volumetric Fog Rendering

Käesoleva bakalaureusetöö eesmärgiks oli kirjeldada udu füüsikalist käitumist looduses ja koostada algoritm udu implementeerimiseks arvutigraafika rakendustes. Töö raames on koostatud volumeetrilist udu renderdav rakendus Unity mängumootoris. Töös hinnatakse loodud rakenduse jõudlust ning analüüsitakse tulemusi. Samuti tuuakse töös ettepanekuid volumeetrilise udu renderdamise täiustamiseks tulevikus.The aim of this bachelor’s thesis is to describe the physical behavior of fog in real life and the algorithm for implementing fog in computer graphics applications. An implementation of the volumetric fog algorithm written in the Unity game engine is also provided. The performance of the implementation is evaluated using benchmarks, including an analysis of the results. Additionally, some suggestions are made to improve the volumetric fog render-ing in the future

Computer vision methods for underwater pipeline segmentation

Underwater pipeline inspection is usually conducted by Remotely Operated Vehicles (ROVs) equipped mainly with optical and acoustic sensors. During long inspections periods and low visibility conditions, traditional visual inspection becomes a tedious job and can lead to operator misinterpretations. Therefore, the automation of this process involves an improvement in the maintenance of the pipelines. This work presents an underwater pipeline segmentation system for rigid pipelines using a monocular camera. A color based edge detector was proposed, taking advantage of the pipeline geometry restrictions, besides tracking information. Segmented pipelines were transformed into a 2D top view representation. The system was evaluated with a dataset containing 7808 images, manually annotated, acquired during real inspection tasks. The system reached 96.5% of detection rate and 96.3% of segmentation accuracy.O processo de inspeção de tubulações submarinas é geralmente realizado por Veículos Operados Remotamente (ROVs) equipados principalmente com sensores óticos e acústicos. Durante longos períodos de inspeção e em condições de baixa visibilidade, o processo de inspeção visual torna-se cansativo e sujeito a falhas de interpretação por parte do operador. Portanto, a automação desse processo apresenta uma melhoria na manutenção das tubulações. Este trabalho apresenta um sistema de segmentação de tubulações rígidas submarinas usando uma câmera monocular. Um detector de bordas baseado na cor foi proposto aproveitando as restrições da geometria das tubulações e informações de rastreamento. Tubulações segmentadas foram transformadas em uma representação de vista superior 2D. O sistema foi avaliado com um conjunto de dados de 7808 imagens, anotados manualmente, obtidas em diferentes tarefas de inspeção reais. O sistema obteve 96.5% na taxa de detecção e 96,3% de acurácia na segmentação