Fast Method Based on Fuzzy Logic for Gaussian-Impulsive Noise Reduction in CT Medical Images

To remove Gaussian-impulsive mixed noise in CT medical images, a parallel filter based on fuzzy logic is applied. The used methodology is structured in two steps. A method based on a fuzzy metric is applied to remove the impulsive noise at the first step. To reduce Gaussian noise, at the second step, a fuzzy peer group filter is used on the filtered image obtained at the first step. A comparative analysis with state-of-the-art methods is performed on CT medical images using qualitative and quantitative measures evidencing the effectiveness of the proposed algorithm. The parallel method is parallelized on shared memory multiprocessors. After applying parallel computing strategies, the obtained computing times indicate that the introduced filter enables to reduce Gaussian-impulse mixed noise on CT medical images in real-time.This research was funded by the Spanish Ministry of Science, Innovation and Universities (Grant RTI2018-098156-B-C54), and it was co-financed with FEDER funds

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method

Holographic Fourier domain diffuse correlation spectroscopy

Diffuse correlation spectroscopy (DCS) is a non-invasive optical modality which can be used to measure cerebral blood flow (CBF) in real-time. It has important potential applications in clinical monitoring, as well as in neuroscience and the development of a non-invasive brain-computer interface. However, a trade-off exists between the signal-to-noise ratio (SNR) and imaging depth, and thus CBF sensitivity, of this technique. Additionally, as DCS is a diffuse optical technique, it is limited by a lack of inherent depth discrimination within the illuminated region of each source-detector pair, and the CBF signal is therefore also prone to contamination by the extracerebral tissues which the light traverses. Placing a particular emphasis on scalability, affordability, and robustness to ambient light, in this work I demonstrate a novel approach which fuses the fields of digital holography and DCS: holographic Fourier domain DCS (FD-DCS). The mathematical formalism of FD-DCS is derived and validated, followed by the construction and validation (for both in vitro and in vivo experiments) of a holographic FD-DCS instrument. By undertaking a systematic SNR performance assessment and developing a novel multispeckle denoising algorithm, I demonstrate the highest SNR gain reported in the DCS literature to date, achieved using scalable and low-cost camera-based detection. With a view to generating a forward model for holographic FD-DCS, in this thesis I propose a novel framework to simulate statistically accurate time-integrated dynamic speckle patterns in biomedical optics. The solution that I propose to this previously unsolved problem is based on the Karhunen-Loève expansion of the electric field, and I validate this technique against novel expressions for speckle contrast for different forms of homogeneous field. I also show that this method can readily be extended to cases with spatially varying sample properties, and that it can also be used to model optical and acoustic parameters

Enhanced algorithms for lesion detection and recognition in ultrasound breast images

Mammography is the gold standard for breast cancer detection. However, it has very high false positive rates and is based on ionizing radiation. This has led to interest in using multi-modal approaches. One modality is diagnostic ultrasound, which is based on non-ionizing radiation and picks up many of the cancers that are generally missed by mammography. However, the presence of speckle noise in ultrasound images has a negative effect on image interpretation. Noise reduction, inconsistencies in capture and segmentation of lesions still remain challenging open research problems in ultrasound images. The target of the proposed research is to enhance the state-of-art computer vision algorithms used in ultrasound imaging and to investigate the role of computer processed images in human diagnostic performance. [Continues.

Computational Tools for Image Processing, Integration, and Visualization of Simultaneous OCT-FLIM Images of Tissue

Multimodal imaging systems have emerged as robust methods for the characterization of atherosclerotic plaques and early diagnosis of oral cancer. Multispectral wide-field Fluorescence Lifetime Imaging Microscopy (FLIM) has been shown to be a capable optical imaging modality for biomedical diagnosis oral cancer. A fiber-based endoscope combined with an intensified charge-coupled device (ICCD) allows to collect and split the fluorescence emission into multiple bands, from which the fluorescence lifetime decay in each spectral channel can be calculated separately. However, for accurate calculations, it is necessary to gather multiple gates increasing the imaging time. Since this time is critical for real-time in vivo applications. This study presents a novel approach to using Rapid Lifetime Determination (RLD) methods to considerably shorten this time period. Moreover, the use of a dual-modality system, incorporating Optical Coherence Tomography (OCT) and FLIM, which simultaneously characterizes 3-D tissue morphology and biochemical composition of tissue, leads to the development of robust computational tools for image processing, integration, and visualization of these imaging techniques. OCTFLIM systems provide 3D structural and 2D biochemical tissue information, which the software tools developed in this work properly integrate to assist the image processing, characterization, and visualization of OCT-FLIM images of atherosclerotic plaques. Additionally, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present two novel computational methods for automated intravascular (IV) OCT plaque characterization. The first method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The second method is intended to automatically identify macrophage/foam cell clusters in atherosclerotic plaques. Vulnerable plaques are characterized by presenting a necrotic core below a thin fibrous cap, and extensive infiltration of macrophages/foam cells. Thus, the degree of macrophage accumulation is an indicator in determining plaque progression and probability of rupture. In this work, two texture features are introduced, the normalized standard deviation ratio (NSDRatio) and the entropy ratio (ENTRatio), to effectively classify areas in the plaque with macrophage/foam cell infiltration. Since this methodology has low complexity and computational cost, it could be implemented for in vivo real time identification of macrophage/foam cell presence

Computational Tools for Image Processing, Integration, and Visualization of Simultaneous OCT-FLIM Images of Tissue

Multimodal imaging systems have emerged as robust methods for the characterization of atherosclerotic plaques and early diagnosis of oral cancer. Multispectral wide-field Fluorescence Lifetime Imaging Microscopy (FLIM) has been shown to be a capable optical imaging modality for biomedical diagnosis oral cancer. A fiber-based endoscope combined with an intensified charge-coupled device (ICCD) allows to collect and split the fluorescence emission into multiple bands, from which the fluorescence lifetime decay in each spectral channel can be calculated separately. However, for accurate calculations, it is necessary to gather multiple gates increasing the imaging time. Since this time is critical for real-time in vivo applications. This study presents a novel approach to using Rapid Lifetime Determination (RLD) methods to considerably shorten this time period. Moreover, the use of a dual-modality system, incorporating Optical Coherence Tomography (OCT) and FLIM, which simultaneously characterizes 3-D tissue morphology and biochemical composition of tissue, leads to the development of robust computational tools for image processing, integration, and visualization of these imaging techniques. OCTFLIM systems provide 3D structural and 2D biochemical tissue information, which the software tools developed in this work properly integrate to assist the image processing, characterization, and visualization of OCT-FLIM images of atherosclerotic plaques. Additionally, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present two novel computational methods for automated intravascular (IV) OCT plaque characterization. The first method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The second method is intended to automatically identify macrophage/foam cell clusters in atherosclerotic plaques. Vulnerable plaques are characterized by presenting a necrotic core below a thin fibrous cap, and extensive infiltration of macrophages/foam cells. Thus, the degree of macrophage accumulation is an indicator in determining plaque progression and probability of rupture. In this work, two texture features are introduced, the normalized standard deviation ratio (NSDRatio) and the entropy ratio (ENTRatio), to effectively classify areas in the plaque with macrophage/foam cell infiltration. Since this methodology has low complexity and computational cost, it could be implemented for in vivo real time identification of macrophage/foam cell presence

Level Set Methods for MRE Image Processing and Analysis

Ph.DDOCTOR OF PHILOSOPH

Laparoscopic Image Recovery and Stereo Matching

Laparoscopic imaging can play a significant role in the minimally invasive surgical procedure. However, laparoscopic images often suffer from insufficient and irregular light sources, specular highlight surfaces, and a lack of depth information. These problems can negatively influence the surgeons during surgery, and lead to erroneous visual tracking and potential surgical risks. Thus, developing effective image-processing algorithms for laparoscopic vision recovery and stereo matching is of significant importance. Most related algorithms are effective on nature images, but less effective on laparoscopic images. The first purpose of this thesis is to restore low-light laparoscopic vision, where an effective image enhancement method is proposed by identifying different illumination regions and designing the enhancement criteria for desired image quality. This method can enhance the low-light region by reducing noise amplification during the enhancement process. In addition, this thesis also proposes a simplified Retinex optimization method for non-uniform illumination enhancement. By integrating the prior information of the illumination and reflectance into the optimization process, this method can significantly enhance the dark region while preserving naturalness, texture details, and image structures. Moreover, due to the replacement of the total variation term with two $l_2$-norm terms, the proposed algorithm has a significant computational advantage. Second, a global optimization method for specular highlight removal from a single laparoscopic image is proposed. This method consists of a modified dichromatic reflection model and a novel diffuse chromaticity estimation technique. Due to utilizing the limited color variation of the laparoscopic image, the estimated diffuse chromaticity can approximate the true diffuse chromaticity, which allows us to effectively remove the specular highlight with texture detail preservation. Third, a robust edge-preserving stereo matching method is proposed, based on sparse feature matching, left and right illumination equalization, and refined disparity optimization processes. The sparse feature matching and illumination equalization techniques can provide a good disparity map initialization so that our refined disparity optimization can quickly obtain an accurate disparity map. This approach is particularly promising on surgical tool edges, smooth soft tissues, and surfaces with strong specular highlight