Hybrid Intelligent System for Diagnosing Breast Pre-Cancerous and Cancerous Conditions Based on Image Analysis

Modern diagnostic technologies are automated microscopy systems (AMSs). In this research study, the authors analyzed the modern AMS methods and algorithms. Criteria-based comparative analysis of AMS has been made, and their advantages and disadvantages have been identified at the three main levels of image processing. This allowed determining the main direction of such systems development, that is, designing the hybrid intelligent AMS. The work of an expert physician implies visual image interpretation, selection of qualitative features of micro-objects, the formation of diagnostic rules based on expert knowledge, and making diagnoses. Knowledge introduction model contains a productive model, in which knowledge is presented in the form of rules expressed in productive pseudo code if-then. Logic inference machine is a module designed to logically derive the facts and rules from the base according to the laws of formal logic. A set of modern methods and algorithms for low-, mid-, and high-level image processing have been used in the AMS structure

An Adaptive Algorithm to Identify Ambiguous Prostate Capsule Boundary Lines for Three-Dimensional Reconstruction and Quantitation

Currently there are few parameters that are used to compare the efficiency of different methods of cancerous prostate surgical removal. An accurate assessment of the percentage and depth of extra-capsular soft tissue removed with the prostate by the various surgical techniques can help surgeons determine the appropriateness of surgical approaches. Additionally, an objective assessment can allow a particular surgeon to compare individual performance against a standard. In order to facilitate 3D reconstruction and objective analysis and thus provide more accurate quantitation results when analyzing specimens, it is essential to automatically identify the capsule line that separates the prostate gland tissue from its extra-capsular tissue. However the prostate capsule is sometimes unrecognizable due to the naturally occurring intrusion of muscle and connective tissue into the prostate gland. At these regions where the capsule disappears, its contour can be arbitrarily reconstructed by drawing a continuing contour line based on the natural shape of the prostate gland. Presented here is a mathematical model that can be used in deciding the missing part of the capsule. This model approximates the missing parts of the capsule where it disappears to a standard shape by using a Generalized Hough Transform (GHT) approach to detect the prostate capsule. We also present an algorithm based on a least squares curve fitting technique that uses a prostate shape equation to merge previously detected capsule parts with the curve equation to produce an approximated curve that represents the prostate capsule. We have tested our algorithms using three shapes on 13 prostate slices that are cut at different locations from the apex and the results are promisin

A review of hough transform and line segment detection approaches

In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

A review of hough transform and line segment detection approaches

In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

Experimental Characterization of Vascular Tissue Viscoelasticity with Emphasis on Elastin's Role

Elucidating how cardiovascular biomechanics is regulated during health and disease is critical for developing diagnostic and therapeutic methods. The extracellular matrix of cardiovascular tissue is composed of multiple fibrillar networks embedded in an amorphous ground substance and has been found to reveal time-dependent mechanical behavior. Given the multiscale nature of tissue biomechanics, an accurate description of cardiovascular biomechanics can be obtained only when microstructural morphology is characterized and put together in correlation with tissue-scale mechanics. This study constitutes the initial steps toward a full description of cardiovascular tissue biomechanics by examining two fundamental questions: How does the elastin microstructure change with tissue-level deformations? And how does the extracellular matrix composition affect tissue biomechanics? The outcome of this dissertation is believed to contribute to the field of cardiovascular tissue biomechanics by addressing some of the fundamental existing questions therein. Assessing alterations in microstructural morphology requires quantified measures which can be challenging given the complex, local and interconnected conformations of tissue structural components embedded in the extracellular matrix. In this study, new image-based methods for quantification of tissue microstructure were developed and examined on aortic tissue under different deformation states. Although in their infancy stages of development, the methods yielded encouraging results consistent with existing perceptions of tissue deformation. Changes in microstructure were investigated by examining histological images of deformed and undeformed tissues. The observations shed light on roles of elastin network in regulating tissue deformation. The viscoelastic behavior of specimens was studied using native, collagen-denatured, and elastin-isolated aortic tissues. The stress-relaxation responses of specimens provide insight into the significance of extracellular matrix composition on tissue biomechanics and how the tissue hydration affects the relaxation behavior. The responses were approximated by traditional spring-dashpot models and the results were interpreted in regards to microstructural composition

Model-based object recognition from a complex binary imagery using genetic algorithm

This paper describes a technique for model-based object recognition in a noisy and cluttered environment, by extending the work presented in an earlier study by the authors. In order to accurately model small irregularly shaped objects, the model and the image are represented by their binary edge maps, rather then approximating them with straight line segments. The problem is then formulated as that of finding the best describing match between a hypothesized object and the image. A special form of template matching is used to deal with the noisy environment, where the templates are generated on-line by a Genetic Algorithm. For experiments, two complex test images have been considered and the results when compared with standard techniques indicate the scope for further research in this direction

A parallel windowing approach to the Hough transform for line segment detection

In the wide range of image processing and computer vision problems, line segment detection has always been among the most critical headlines. Detection of primitives such as linear features and straight edges has diverse applications in many image understanding and perception tasks. The research presented in this dissertation is a contribution to the detection of straight-line segments by identifying the location of their endpoints within a two-dimensional digital image. The proposed method is based on a unique domain-crossing approach that takes both image and parameter domain information into consideration. First, the straight-line parameters, i.e. location and orientation, have been identified using an advanced Fourier-based Hough transform. As well as producing more accurate and robust detection of straight-lines, this method has been proven to have better efficiency in terms of computational time in comparison with the standard Hough transform. Second, for each straight-line a window-of-interest is designed in the image domain and the disturbance caused by the other neighbouring segments is removed to capture the Hough transform buttery of the target segment. In this way, for each straight-line a separate buttery is constructed. The boundary of the buttery wings are further smoothed and approximated by a curve fitting approach. Finally, segments endpoints were identified using buttery boundary points and the Hough transform peak. Experimental results on synthetic and real images have shown that the proposed method enjoys a superior performance compared with the existing similar representative works