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

Reconstructing vectorised photographic images

We address the problem of representing captured images in the continuous mathematical space more usually associated with certain forms of drawn ('vector') images. Such an image is resolution-independent so can be used as a master for varying resolution-specific formats. We briefly describe the main features of a vectorising codec for photographic images, whose significance is that drawing programs can access images and image components as first-class vector objects. This paper focuses on the problem of rendering from the isochromic contour form of a vectorised image and demonstrates a new fill algorithm which could also be used in drawing generally. The fill method is described in terms of level set diffusion equations for clarity. Finally we show that image warping is both simplified and enhanced in this form and that we can demonstrate real histogram equalisation with genuinely rectangular histograms

Optimization of video capturing and tone mapping in video camera systems

Image enhancement techniques are widely employed in many areas of professional and consumer imaging, machine vision and computational imaging. Image enhancement techniques used in surveillance video cameras are complex systems involving controllable lenses, sensors and advanced signal processing. In surveillance, a high output image quality with very robust and stable operation under difficult imaging conditions are essential, combined with automatic, intelligent camera behavior without user intervention. The key problem discussed in this thesis is to ensure this high quality under all conditions, which specifically addresses the discrepancy of the dynamic range of input scenes and displays. For example, typical challenges are High Dynamic Range (HDR) and low-dynamic range scenes with strong light-dark differences and overall poor visibility of details, respectively. The detailed problem statement is as follows: (1) performing correct and stable image acquisition for video cameras in variable dynamic range environments, and (2) finding the best image processing algorithms to maximize the visualization of all image details without introducing image distortions. Additionally, the solutions should satisfy complexity and cost requirements of typical video surveillance cameras. For image acquisition, we develop optimal image exposure algorithms that use a controlled lens, sensor integration time and camera gain, to maximize SNR. For faster and more stable control of the camera exposure system, we remove nonlinear tone-mapping steps from the level control loop and we derive a parallel control strategy that prevents control delays and compensates for the non-linearity and unknown transfer characteristics of the used lenses. For HDR imaging we adopt exposure bracketing that merges short and long exposed images. To solve the involved non-linear sensor distortions, we apply a non-linear correction function to the distorted sensor signal, implementing a second-order polynomial with coefficients adaptively estimated from the signal itself. The result is a good, dynamically controlled match between the long- and short-exposed image. The robustness of this technique is improved for fluorescent light conditions, preventing serious distortions by luminance flickering and color errors. To prevent image degradation we propose both fluorescent light detection and fluorescence locking, based on measurements of the sensor signal intensity and color errors in the short-exposed image. The use of various filtering steps increases the detector robustness and reliability for scenes with motion and the appearance of other light sources. In the alternative algorithm principle of fluorescence locking, we ensure that light integrated during the short exposure time has a correct intensity and color by synchronizing the exposure measurement to the mains frequency. The second area of research is to maximize visualization of all image details. This is achieved by both global and local tone mapping functions. The largest problem of Global Tone Mapping Functions (GTMF) is that they often significantly deteriorate the image contrast. We have developed a new GTMF and illustrate, both analytically and perceptually, that it exhibits only a limited amount of compression, compared to conventional solutions. Our algorithm splits GTMF into two tasks: (1) compressing HDR images (DRC transfer function) and (2) enhancing the (global) image contrast (CHRE transfer function). The DRC subsystem adapts the HDR video signal to the remainder of the system, which can handle only a fraction of the original dynamic range. Our main contribution is a novel DRC function shape which is adaptive to the image, so that details in the dark image parts are enhanced simultaneously while only moderately compressing details in the bright areas. Also, the DRC function shape is well matched with the sensor noise characteristics in order to limit the noise amplification. Furthermore, we show that the image quality can be significantly improved in DRC compression if a local contrast preservation step is included. The second part of GTMF is a CHRE subsystem that fine-tunes and redistributes the luminance (and color) signal in the image, to optimize global contrast of the scene. The contribution of the proposed CHRE processing is that unlike standard histogram equalization, it can preserve details in statistically unpopulated but visually relevant luminance regions. One of the important cornerstones of the GTMF is that both DRC and CHRE algorithms are performed in the perceptually uniform space and optimized for the salient regions obtained by the improved salient-region detector, to maximize the relevant information transfer to the HVS. The proposed GTMF solution offers a good processing quality, but cannot sufficiently preserve local contrast for extreme HDR signals and it gives limited improvement low-contrast scenes. The local contrast improvement is based on the Locally Adaptive Contrast Enhancement (LACE) algorithm. We contribute by using multi-band frequency decomposition, to set up the complete enhancement system. Four key problems occur with real-time LACE processing: (1) "halo" artifacts, (2) clipping of the enhancement signal, (3) noise degradation and (4) the overall system complexity. "Halo" artifacts are eliminated by a new contrast gain specification using local energy and contrast measurements. This solution has a low complexity and offers excellent performance in terms of higher contrast and visually appealing performance. Algorithms preventing clipping of the output signal and reducing noise amplification give a further enhancement. We have added a supplementary discussion on executing LACE in the logarithmic domain, where we have derived a new contrast gain function solving LACE problems efficiently. For the best results, we have found that LACE processing should be performed in the logarithmic domain for standard and HDR images, and in the linear domain for low-contrast images. Finally, the complexity of the contrast gain calculation is reduced by a new local energy metric, which can be calculated efficiently in a 2D-separable fashion. Besides the complexity benefit, the proposed energy metric gives better performance compared to the conventional metrics. The conclusions of our work are summarized as follows. For acquisition, we need to combine an optimal exposure algorithm, giving both improved dynamic performance and maximum image contrast/SNR, with robust exposure bracketing that can handle difficult conditions such as fluorescent lighting. For optimizing visibility of details in the scene, we have split the GTMF in two parts, DRC and CHRE, so that a controlled optimization can be performed offering less contrast compression and detail loss than in the conventional case. Local contrast is enhanced with the known LACE algorithm, but the performance is significantly improved by individually addressing "halo" artifacts, signal clipping and noise degradation. We provide artifact reduction by new contrast gain function based on local energy, contrast measurements and noise estimation. Besides the above arguments, we have contributed feasible performance metrics and listed ample practical evidence of the real-time implementation of our algorithms in FPGAs and ASICs, used in commercially available surveillance cameras, which obtained awards for their image quality

Medical Image Contrast Enhancement via Wavelet Homomorphic Filtering Transform

A novel enhancement algorithm for magnetic resonance (MR) images based on spatial homomorphic filtering transform is proposed in this paper. By this method, the source image is decomposed into different sub-images by dyadic wavelet transform. Homomorphic filtering functions are applied in performing filtering of corresponding sub-band images to attenuate the low frequencies as well as amplify the high frequencies, and a linear adjustment is carried out on the low frequency of the highest level. Later, inverse dyadic wavelet transform is applied to reconstruct the object image. Experiment results on MR images illustrate that the proposed method can eliminate non-uniformity luminance distribution effectively, some subtle tissues can be improved effectually, and some weak sections have not been smoothed by the novel method.

A Study on Geometry Contrast Enhancement for 3D Point Models

Electrical EngineeringPoint primitives have come into the spotlight as a representation method of 3D models. A lot of researches have been performed on the modeling, processing, and rendering 3D point models. Especially, various methods have been developed for the extraction and preservation of the salient features of corners, curves, and edges in 3D point models. However, little effort has been made to extract and enhance the weak features that are relatively imperceptible due to the low geometry contrast. In this thesis, we propose a novel method to improve the visibility of 3D point models by enhancing the geometry contrast of weak features. We first define a weak feature region as a group of local points yielding small deviations of normal directions. Then we define the geometry histogram for each region as the distribution of the signed distance between a feature point and the locally approximated plane. We equalize and stretch the geometry histogram and move the corresponding feature points accordingly. We also render the enhanced model using the normal mapping for better visual presentation. Experimental results demonstrate that the proposed method enhances the geometry contrast of 3D point models by refining the appearance of the weak features. We expect that the geometry contrast enhancement algorithm will facilitate many applications in various fields.ope

Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

Detection of larynx cancer from medical imaging is important for the quantification and for the definition of target volumes in radiotherapy treatment planning (RTP). Magnetic resonance imaging (MRI) is being increasingly used in RTP due to its high resolution and excellent soft tissue contrast. Manually detecting larynx cancer from sequential MRI is time consuming and subjective. The large diversity of cancer in terms of geometry, non-distinct boundaries combined with the presence of normal anatomical regions close to the cancer regions necessitates the development of automatic and robust algorithms for this task. A new automatic algorithm for the detection of larynx cancer from 2D gadoliniumenhanced T1-weighted (T1+Gd) MRI to assist clinicians in RTP is presented. The algorithm employs edge detection using spatial neighborhood information of pixels and incorporates this information in a fuzzy c-means clustering process to robustly separate different tissues types. Furthermore, it utilizes the information of the expected cancerous location for cancer regions labeling. Comparison of this automatic detection system with manual clinical detection on real T1+Gd axial MRI slices of 2 patients (24 MRI slices) with visible larynx cancer yields an average dice similarity coefficient of 0.78±0.04 and average root mean square error of 1.82±0.28 mm. Preliminary results show that this fully automatic system can assist clinicians in RTP by obtaining quantifiable and non-subjective repeatable detection results in a particular time-efficient and unbiased fashion

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus