Color Image Segmentation Using Fuzzy C-Regression Model

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms

Exploring the Effects of Hemodialysis on Renal and Hepatic Blood Flow and Function using CT Perfusion Imaging

Hemodialysis (HD) is the most common form of renal replacement therapy for end-stage renal disease. However, patients develop complications that are driven by HD-induced circulatory stress from rapidly removing large fluid volumes during HD, making various vascular beds vulnerable to ischemia. By assessing how HD-induced circulatory stress affects different organs, it may be possible to characterize the mechanisms behind these complications and evaluate therapeutic interventions. This thesis aims to explore how HD affects renal and hepatic blood flow and function using CT perfusion imaging. For this work, patients received either standard or cooled HD first in a two-visit, crossover study design, where imaging was performed before, during and after each HD session. Residual renal function is linked to improved clinical outcomes, yet characteristically declines upon HD initiation. In the first thesis project, we determined that renal perfusion deceases during HD, which could be an early manifestation of HD-mediated residual renal function loss. Although the liver normally clears endotoxin, increased circulating endotoxin levels have been found in HD patients. In the second thesis project, we showed that concurrent hepatic perfusion redistribution and decreased liver function during HD are likely responsible for increased circulating toxin levels. Dialysate cooling is a low-cost, feasible intervention that ameliorates HD-induced circulatory stress. In the first and second thesis projects, we found that cooling trended towards mitigating the drop in renal perfusion during HD and ameliorating the changes in liver perfusion and function during HD. If it were possible to accurately assess glomerular filtration rate (GFR) in HD patients, HD prescriptions could be adjusted in accordance with residual renal function to preserve remaining function. In the third thesis project, we extended the CT perfusion technique to measure GFR in HD patients, yielding physiologically realistic GFR values, thus demonstrating the feasibility of this approach in terms of reliability and accuracy. These findings help explain residual renal function loss and endotoxemia in HD patients, and showcases the protective potential of dialysate cooling. In addition, this work demonstrates the benefit of using CT perfusion as a functional imaging technique to further characterize and evaluate therapies for end-stage renal disease pathologies

2023 Summer Experience Program Abstracts

https://openworks.mdanderson.org/sumexp23/1130/thumbnail.jp

A Deconvolution Framework with Applications in Medical and Biological Imaging

A deconvolution framework is presented in this thesis and applied to several problems in medical and biological imaging. The framework is designed to contain state of the art deconvolution methods, to be easily expandable and to combine different components arbitrarily. Deconvolution is an inverse problem and in order to cope with its ill-posed nature, suitable regularization techniques and additional restrictions are required. A main objective of deconvolution methods is to restore degraded images acquired by fluorescence microscopy which has become an important tool in biological and medical sciences. Fluorescence microscopy images are degraded by out-of-focus blurring and noise and the deconvolution algorithms to restore these images are usually called deblurring methods. Many deblurring methods were proposed to restore these images in the last decade which are part of the deconvolution framework. In addition, existing deblurring techniques are improved and new components for the deconvolution framework are developed. A considerable improvement could be obtained by combining a state of the art regularization technique with an additional non-negativity constraint. A real biological screen analysing a specific protein in human cells is presented and shows the need to analyse structural information of fluorescence images. Such an analysis requires a good image quality which is the aim of the deblurring methods if the required image quality is not given. For a reliable understanding of cells and cellular processes, high resolution 3D images of the investigated cells are necessary. However, the ability of fluorescence microscopes to image a cell in 3D is limited since the resolution along the optical axis is by a factor of three worse than the transversal resolution. Standard microscopy image deblurring techniques are able to improve the resolution but the problem of a lower resolution in direction along the optical axis remains. It is however possible to overcome this problem using Axial Tomography providing tilted views of the object by rotating it under the microscope. The rotated images contain additional information about the objects which can be used to improve the resolution along the optical axis. In this thesis, a sophisticated method to reconstruct a high resolution Axial Tomography image on basis of the developed deblurring methods is presented. The deconvolution methods are also used to reconstruct the dose distribution in proton therapy on basis of measured PET images. Positron emitters are activated by proton beams but a PET image is not directly proportional to the delivered radiation dose distribution. A PET signal can be predicted by a convolution of the planned dose with specific filter functions. In this thesis, a dose reconstruction method based on PET images which reverses the convolution approach is presented and the potential to reconstruct the actually delivered dose distribution from measured PET images is investigated. Last but not least, a new denoising method using higher-order statistic information of a given Gaussian noise signal is presented and compared to state of the art denoising methods

Head and Neck Critical Illness

There are various malignant tumors in the head and neck area, including oral cavity, pharynx, sinonasal cavity, and salivary glands. Squamous cell carcinoma is the most common cancer among head and neck cancers. In salivary glands, there are many types of malignancies that can develop, such as malignant lymphoma, adenoid cystic carcinoma, adenocarcinoma, and mesenchymal tumors. In a clinical setting, imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), is very important in terms of the prediction of the histological type and the evaluation of the extent of invasion of adjacent structures. In basic research, there are few animal models in head and neck malignancies. In this Special Issue, we broadly discuss the basic and clinical research in head and neck malignancies

Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits

This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed. Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach. Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms. Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature. Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound