COMPUTER-AIDED QUANTITATIVE EARLY DIAGNOSIS OF DIABETIC FOOT

Diabetes is an incurable metabolic disease characterized by high blood sugar levels. The feet of people with diabetes are at the risk of a variety of pathological consequences including peripheral vascular disease, deformity, ulceration, and ultimately amputation. The key to managing the diabetic foot is prevention and early detection. Unfortunately, current hospital centered reactive diabetes care and the availability of inadequate qualitative diagnostic screening procedures causes physicians to miss the diagnosis in 61% of the patients. We have developed a computer aided diagnostic system for early detection of diabetic foot. The key idea is that diabetic foot exhibits significant neuropathic and vascular damages. When a diabetic foot is placed under cold stress, the thermal recovery will be much slower. This thermal recovery speed can be a quantitative measure for the diagnosis of diabetic foot condition. In our research, thermal recovery of the feet following cold stress is captured using an infrared camera. The captured infrared video is then filtered, segmented, and registered. The temperature recovery at each point on the foot is extracted and analyzed using a thermal regulation model, and the problematic regions are identified. In this thesis, we present our research on the following aspects of the developed computer aided diagnostic systems: subject measurement protocols, a trustful numerical model of the camera noise and noise parameter estimations, infrared video segmentation, new models of thermal regulations, thermal patterns classifications, and our preliminary findings based on small scale clinical study of about 40 subjects, which demonstrated the potential the new diagnostic system

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Finite element based interpolation methods for spatial and temporal resolution enhancement for image sequences

Spatial resolution enhancement is a process for reconstructing a high resolution image from a low resolution image, whereas temporal resolution enhancement of encoded video aims of interpolating the skipped frames, making use of two successively received frames. In this thesis, a new image interpolation model, called the generalized image interpolation model , is developed in order to devise new techniques for spatial resolution enhancement of images, and temporal resolution enhancement of encoded video sequences. The interpolation model is based on the finite element method, and takes into account the unknown neighboring pixels, and therefore is capable of interpolating a collection of unknown pixels with an arbitrary shape, while providing a spatial continuity between the unknown pixels. Based on the generalized interpolation model, an edge-preserving iterative refinement scheme for spatial resolution enhancement of images is proposed. This scheme exploits not only the neighboring pixels whose values are known, but also takes into account those with unknown values. It is shown that the edge-preserving iterative refinement process maintains the smooth variation along a dominant edge in the up-scaled image. Simulation results show that the proposed scheme results in up-scaled images with subjective and objective qualities, which are better than those of the existing interpolation schemes. Further, the scheme is also shown to be capable of up-scaling an image by an arbitrary magnification factor, without resorting to extra steps, or the use of any conventional interpolation method. Next, error concealment-based MCI schemes are also presented for temporal resolution enhancement of encoded video sequences. These schemes are also based on the generalized image interpolation model, and need no pixel classification, thus reducing substantially the computational complexity. They are shown to be capable of concealing the errors in the homogeneous regions as well as in regions containing sharp edges. Experiments are carried out showing that the proposed schemes result in reconstructed frames having a better visual quality and a lower computational complexity than that provided by the existing techniques

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Analysis of Cellular and Subcellular Morphology using Machine Learning in Microscopy Images

Human cells undergo various morphological changes due to progression in the cell-cycle or environmental factors. Classification of these morphological states is vital for effective clinical decisions. Automated classification systems based on machine learning models are data-driven and efficient and help to avoid subjective outcomes. However, the efficacy of these models is highly dependent on the feature description along with the amount and nature of the training data. This thesis presents three studies of automated image-based classification of cellular and subcellular morphologies. The first study presents 3D Sorted Random Projections (SRP) which includes the proposed approach to compute 3D plane information for texture description of 3D nuclear images. The proposed 3D SRP is used to classify nuclear morphology and measure changes in heterochromatin, which in turn helps to characterise cellular states. Classification performance evaluated on 3D images of the human fibroblast and prostate cancer cell lines shows that 3D SRP provides better classification than other feature descriptors. The second study is on imbalanced multiclass and single-label classification of blood cell images. The scarcity of minority sam ples causes a drop in classification performance on minority classes. This study proposes oversampling of minority samples us ing data augmentation approaches, namely mixup, WGAN-div and novel nonlinear mixup, along with a minority class focussed sampling strategy. Classification performance evaluated using F1-score shows that the proposed deep learning framework out performs state-of-the art approaches on publicly available images of human T-lymphocyte cells and red blood cells. The third study is on protein subcellular localisation, which is an imbalanced multiclass and multilabel classification problem. In order to handle data imbalance, this study proposes an oversampling method which includes synthetic images constructed using nonlinear mixup and geometric/colour transformations. The regularisation capability of nonlinear mixup is further improved for protein images. In addition, an imbalance aware sampling strategy is proposed to identify minority and medium classes in the dataset and include them during training. Classification performance evaluated on the Human Protein Atlas Kaggle challenge dataset using F1-score shows that the proposed deep learning framework achieves better predictions than existing methods

MATLAB

A well-known statement says that the PID controller is the "bread and butter" of the control engineer. This is indeed true, from a scientific standpoint. However, nowadays, in the era of computer science, when the paper and pencil have been replaced by the keyboard and the display of computers, one may equally say that MATLAB is the "bread" in the above statement. MATLAB has became a de facto tool for the modern system engineer. This book is written for both engineering students, as well as for practicing engineers. The wide range of applications in which MATLAB is the working framework, shows that it is a powerful, comprehensive and easy-to-use environment for performing technical computations. The book includes various excellent applications in which MATLAB is employed: from pure algebraic computations to data acquisition in real-life experiments, from control strategies to image processing algorithms, from graphical user interface design for educational purposes to Simulink embedded systems

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems