Detection of the Optic Disc in Images of the Retina Using Gabor Filters and Phase Portrait Analysis

Abstract Several industrial chemical processes exhibit severe nonlinearity. This paper addresses the computational and nonlinear issues occurring in many typical industrial problems in aspects of its stability, strength of nonlinearity and input output dynamics. In this article, initially, a prospective investigation is conducted on various nonlinear processes through phase portrait analysis to understand their stability status at different initial conditions about the vicinity of the operating point of the process. To estimate the degree of nonlinearity, for input perturbations from its nominal value, a novel nonlinear measure is put forward, that anticipates on the converging area between the nonlinear and their linearized responses. The nonlinearity strength is fixed between 0 and 1 to classify processes to be mild, medium or highly nonlinear. The most suitable operating point, for which the system remains asymptotically stable is clearly identified from the phase portrait. The metric can be contemplated as a promising tool to measure the nonlinearity of Industrial case studies at different linear approximations. Numerical simulations are executed in Matlab to compute , which conveys that the nonlinear dynamics of each Industrial example is very sensitive to input perturbations at different linear approximations. In addition to the identified metric, nonlinear lemmas are framed to select appropriate control schemes for the processes based on its numerical value of nonlinearity..

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Phase Portrait Analysis of Continuous Stirred Tank Reactor (CSTR)

Nanoelectronics, Circuits and Communication Systems - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-15-7486-3_18 ◽

2020 ◽

pp. 177-192

Author(s):

Ankita Ranjan ◽

Sarbani Chakraborty

Keyword(s):

Phase Portrait ◽

Stirred Tank ◽

Continuous Stirred Tank Reactor ◽

Stirred Tank Reactor ◽

Tank Reactor ◽

Phase Portrait Analysis ◽

Continuous Stirred Tank

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Development of a Mobile Integrated System for Thermal Imaging Videodigital Diagnostics of Cow Diseases

Machinery and Equipment for Rural Area ◽

10.33267/2072-9642-2021-6-23-28 ◽

2021 ◽

pp. 23-28

Author(s):

Yu.A. Tsoi ◽

◽

V.E. Lyubimov ◽

L.D. Saginov ◽

V.V. Kirsanov ◽

...

Keyword(s):

Phase Portrait ◽

Thermal Imaging ◽

Block Diagram ◽

Integrated System ◽

Histogram Analysis ◽

Thermal Images ◽

New Methods ◽

Inflammatory Processes ◽

Phase Portrait Analysis ◽

Thermal Imaging Equipment

The principles of creating a mobile integrated system for thermal imaging video-digital diagnostics of cow diseases, a block diagram of a mobile integrated system and a sequence of operations are described. The parameters of thermal imaging equipment for detecting temperature anomalies have been determined. Methods of segmentation of thermal images, histogram analysis and new methods of phase portrait analysis are selected. The features of the formation and processing of thermal images for monitoring inflammatory processes in animals have been determined.

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Digital Image Processing and Machine Learning Techniques for the Detection of Architectural Distortion in Prior Mammograms

Machine Learning in Computer-Aided Diagnosis - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-4666-0059-1.ch002 ◽

2012 ◽

pp. 23-65 ◽

Cited By ~ 1

Author(s):

Shantanu Banik ◽

Rangaraj M. Rangayyan ◽

J. E. Leo Desautels

Keyword(s):

Breast Cancer ◽

Phase Portrait ◽

Interval Cancer ◽

Gabor Filters ◽

Bayesian Classifier ◽

Angular Spread ◽

Architectural Distortion ◽

Screening Mammograms ◽

Prior Mammograms ◽

Receiver Operating

Architectural distortion is a subtle but important early sign of breast cancer. The purpose of this study is to develop methods for the detection of sites of architectural distortion in prior mammograms of interval-cancer cases. Screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular architectural distortion. The methods for the detection of architectural distortion are based upon Gabor filters, phase portrait analysis, a novel method for the analysis of the angular spread of power, fractal analysis via Fractal Dimension (FD), structural analysis of texture via Laws’ texture energy measures derived from geometrically transformed regions of interest (ROIs), and statistical analysis of texture using Haralick’s 14 texture features. The application of Gabor filters and linear phase portrait modeling was used to detect initial candidates of sites of architectural distortion; 4,224 ROIs, including 301 true-positive ROIs related to architectural distortion, were automatically obtained from 106 prior mammograms of 56 interval-cancer cases and from 52 mammograms of 13 normal cases. For each ROI, the FD, three measures of angular spread of power, 10 Laws’ measures, and 14 Haralick’s features were computed. The areas under the receiver operating characteristic curves obtained using the features selected by stepwise logistic regression and the leave-one-ROI-out method are 0.76 with the Bayesian classifier, 0.75 with Fisher linear discriminate analysis, and 0.78 with a single-layer feed forward neural network. Free-response receiver operating characteristics indicated sensitivities of 0.80 and 0.90 at 5.8 and 8.1 false positives per image, respectively, with the Bayesian classifier and the leave-one-image-out method. The methods have shown good potential in detecting architectural distortion in mammograms of interval-cancer cases.

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