Hierarchical stack filtering : a bitplane-based algorithm for massively parallel processors

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition, and then process every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement

Bit-plane stack filter algorithm for focal plane processors

This work presents a novel parallel technique to implement stack morphological filters for image processing. The method relies on applying the image bitwise decomposition to manipulate the grayscale image at a bit-plane level, while simple logical operations and Positive Boolean Functions (PBF’s) are executed in parallel to derive the transformed bit-planes. The relationship between the bitwise and threshold decomposition is closely investigated and analysed, which lead us to derive an algorithm whose control flow is full binary encoded. Furthermore, the algorithm exhibits an interesting performance, which depends on the image histogram thanks to its hierarchical processing and the study of the relationship among binary decompositions

Review of Graph, Medical and Color Image base Segmentation Techniques

Abstract-Thi

Digital Hologram Image Processing

In this thesis we discuss and examine the contributions we have made to the field of digital hologram image processing. In particular, we will deal with the processing of numerical reconstructions of real-world three-dimensional macroscopic objects recorded by in-line digital holography. Our selection of in-line digital holography over off-axis digital holography is based primarily on resolution. There is evidence that an off-axis architecture requires approximately four times the resolution to record a hologram than an in-line architecture. The high resolution of holographic film means this is acceptable in optical holography. However, in digital holography the bandwidth of the recording medium is already severely limited and if we are to extract information from reconstructions we need the highest possible resolution which, if one cannot harness the functionality of accurately reconstructing phase, is achieved through using an in-line architecture. Two of the most significant problems encountered with reconstructions of in-line digital holograms include the small depth-of-field of each reconstruction and corruptive influence of the unwanted twin-image. This small depth-of-field makes it difficult to accurately process the numerical reconstructions and it is in this shortcoming that we will make our first three contributions: focusing algorithms, background and object segmentation algorithms and algorithms to create a single image where all object regions are in focus. Using a combination of our focusing algorithms and our background segmentation algorithm, we will make our fourth contribution: a rapid twin-image reduction algorithm for in-line digital holography. We believe that our techniques would be applicable to all digital holographic objects, in particular its relevant to objects where phase unwrapping is not an option. We demonstrate the usefulness of the algorithms for a range of macroscopic objects with varying texture and contrast

Digital Hologram Image Processing

In this thesis we discuss and examine the contributions we have made to the field of digital hologram image processing. In particular, we will deal with the processing of numerical reconstructions of real-world three-dimensional macroscopic objects recorded by in-line digital holography. Our selection of in-line digital holography over off-axis digital holography is based primarily on resolution. There is evidence that an off-axis architecture requires approximately four times the resolution to record a hologram than an in-line architecture. The high resolution of holographic film means this is acceptable in optical holography. However, in digital holography the bandwidth of the recording medium is already severely limited and if we are to extract information from reconstructions we need the highest possible resolution which, if one cannot harness the functionality of accurately reconstructing phase, is achieved through using an in-line architecture. Two of the most significant problems encountered with reconstructions of in-line digital holograms include the small depth-of-field of each reconstruction and corruptive influence of the unwanted twin-image. This small depth-of-field makes it difficult to accurately process the numerical reconstructions and it is in this shortcoming that we will make our first three contributions: focusing algorithms, background and object segmentation algorithms and algorithms to create a single image where all object regions are in focus. Using a combination of our focusing algorithms and our background segmentation algorithm, we will make our fourth contribution: a rapid twin-image reduction algorithm for in-line digital holography. We believe that our techniques would be applicable to all digital holographic objects, in particular its relevant to objects where phase unwrapping is not an option. We demonstrate the usefulness of the algorithms for a range of macroscopic objects with varying texture and contrast

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

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

Image Analysis Algorithms for Single-Cell Study in Systems Biology

With the contiguous shift of biology from a qualitative toward a quantitative field of research, digital microscopy and image-based measurements are drawing increased interest. Several methods have been developed for acquiring images of cells and intracellular organelles. Traditionally, acquired images are analyzed manually through visual inspection. The increasing volume of data is challenging the scope of manual analysis, and there is a need to develop methods for automated analysis. This thesis examines the development and application of computational methods for acquisition and analysis of images from single-cell assays. The thesis proceeds with three different aspects.First, a study evaluates several methods for focusing microscopes and proposes a novel strategy to perform focusing in time-lapse imaging. The method relies on the nature of the focus-drift and its predictability. The study shows that focus-drift is a dynamical system with a small randomness. Therefore, a prediction-based method is employed to track the focus-drift overtime. A prototype implementation of the proposed method is created by extending the Nikon EZ-C1 Version 3.30 (Tokyo, Japan) imaging platform for acquiring images with a Nikon Eclipse (TE2000-U, Nikon, Japan) microscope.Second, a novel method is formulated to segment individual cells from a dense cluster. The method incorporates multi-resolution analysis with maximum-likelihood estimation (MAMLE) for cell detection. The MAMLE performs cell segmentation in two phases. The initial phase relies on a cutting-edge filter, edge detection in multi-resolution with a morphological operator, and threshold decomposition for adaptive thresholding. It estimates morphological features from the initial results. In the next phase, the final segmentation is constructed by boosting the initial results with the estimated parameters. The MAMLE method is evaluated with de novo data sets as well as with benchmark data from public databases. An empirical evaluation of the MAMLE method confirms its accuracy.Third, a comparative study is carried out on performance evaluation of state-ofthe-art methods for the detection of subcellular organelles. This study includes eleven algorithms developed in different fields for segmentation. The evaluation procedure encompasses a broad set of samples, ranging from benchmark data to synthetic images. The result from this study suggests that there is no particular method which performs superior to others in the test samples. Next, the effect of tetracycline on transcription dynamics of tetA promoter in Escherichia coli (E. coli ) cells is studied. This study measures expressions of RNA by tagging the MS2d-GFP vector with a target gene. The RNAs are observed as intracellular spots in confocal images. The kernel density estimation (KDE) method for detecting the intracellular spots is employed to quantify the individual RNA molecules.The thesis summarizes the results from five publications. Most of the publications are associated with different methods for imaging and analysis of microscopy. Confocal images with E. coli cells are targeted as the primary area of application. However, potential applications beyond the primary target are also made evident. The findings of the research are confirmed empirically