scholarly journals COMPARISON OF COMPUTER AIDED IMAGE ANALYSIS METHODS WITH STANDARD PHOTO METHOD FOR DETERMINATION OF MUCK PILE FRAGMENTATION

2018 ◽  
Vol 15 (4) ◽  
pp. 488-495
Author(s):  
Halim Cevizci
Keyword(s):  
Image Analysis ◽  
Analysis Methods ◽  
Computer Aided

COMPUTER-AIDED CHARACTERIZATION OF THYROID NODULES BY IMAGE ANALYSIS METHODS

2003 ◽  
Author(s):  
S. TSANTIS ◽  
I. KALATZIS ◽  
N. PILIOURAS ◽  
D. CAVOURAS ◽  
N. DIMITROPOULOS ◽  
...  
Keyword(s):  
Image Analysis ◽  
Thyroid Nodules ◽  
Analysis Methods ◽  
Computer Aided

An Overview of Biomedical Image Analysis From the Deep Learning Perspective

2020 ◽  
pp. 197-218
Author(s):  
Shouvik Chakraborty ◽  
Kalyani Mali
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Biomedical Image Analysis ◽  
Comprehensive Overview ◽  
Biomedical Image ◽  
Research Areas ◽  
Analysis Methods ◽  
Computer Aided ◽  
Recent Developments ◽  
Active Research

Biomedical image analysis methods are gradually shifting towards computer-aided solutions from manual investigations to save time and improve the quality of the diagnosis. Deep learning-assisted biomedical image analysis is one of the major and active research areas. Several researchers are working in this domain because deep learning-assisted computer-aided diagnostic solutions are well known for their efficiency. In this chapter, a comprehensive overview of the deep learning-assisted biomedical image analysis methods is presented. This chapter can be helpful for the researchers to understand the recent developments and drawbacks of the present systems. The discussion is made from the perspective of the computer vision, pattern recognition, and artificial intelligence. This chapter can help to get future research directions to exploit the blessings of deep learning techniques for biomedical image analysis.

scholarly journals Determination of renewable energy yield from mixed waste material from the use of novel image analysis methods

2013 ◽  
Vol 33 (11) ◽  
pp. 2449-2456 ◽  
Author(s):  
S.T. Wagland ◽  
R. Dudley ◽  
M. Naftaly ◽  
P.J. Longhurst
Keyword(s):  
Image Analysis ◽  
Renewable Energy ◽  
Waste Material ◽  
Energy Yield ◽  
Analysis Methods ◽  
Mixed Waste

Gender determination of caucasoid hair using image analysis

1993 ◽  
Vol 51 ◽  
pp. 216-217
Author(s):  
T.B. Ball ◽  
W.M. Hess
Keyword(s):  
Image Analysis ◽  
Cross Sections ◽  
Scalp Hair ◽  
The Body ◽  
Equivalent Diameter ◽  
Cross Sectional ◽  
Hair Samples ◽  
Length Width ◽  
Morphological Differences

It has been demonstrated that cross sections of bundles of hair can be effectively studied using image analysis. These studies can help to elucidate morphological differences of hair from one region of the body to another. The purpose of the present investigation was to use image analysis to determine whether morphological differences could be demonstrated between male and female human Caucasian terminal scalp hair.Hair samples were taken from the back of the head from 18 caucasoid males and 13 caucasoid females (Figs. 1-2). Bundles of 50 hairs were processed for cross-sectional examination and then analyzed using Prism Image Analysis software on a Macintosh llci computer. Twenty morphological parameters of size and shape were evaluated for each hair cross-section. The size parameters evaluated were area, convex area, perimeter, convex perimeter, length, breadth, fiber length, width, equivalent diameter, and inscribed radius. The shape parameters considered were formfactor, roundness, convexity, solidity, compactness, aspect ratio, elongation, curl, and fractal dimension.

A quantitative image analysis method for the determination of cocontinuity in polymer blends

1993 ◽  
Vol 51 ◽  
pp. 894-895
Author(s):  
William A. Heeschen
Keyword(s):  
Image Analysis ◽  
Polymer Blends ◽  
Quantitative Measurement ◽  
Morphological Evolution ◽  
Phase Ratio ◽  
Irregular Shapes ◽  
Quantitative Image ◽  
Discrete Domains ◽  
A Domains

Two new morphological measurements based on digital image analysis, CoContinuity and CoContinuity Balance, have been developed and implemented for quantitative measurement of morphology in polymer blends. The morphology of polymer blends varies with phase ratio, composition and processing. A typical morphological evolution for increasing phase ratio of polymer A to polymer B starts with discrete domains of A in a matrix of B (A/B < 1), moves through a cocontinuous distribution of A and B (A/B ≈ 1) and finishes with discrete domains of B in a matrix of A (A/B > 1). For low phase ratios, A is often seen as solid convex particles embedded in the continuous B phase. As the ratio increases, A domains begin to evolve into irregular shapes, though still recognizable as separate domains. Further increase in the phase ratio leads to A domains which extend into and surround the B phase while the B phase simultaneously extends into and surrounds the A phase.

MAKSIMASI KEUNTUNGAN USAHA PUKAT CINCIN DI NEGERI LATUHALAT PADA MUSIM TIMUR

2018 ◽  
Vol 2 (1) ◽  
pp. 22-32
Author(s):  
Willem Talakua ◽  
Eygner Gerald Talakua
Keyword(s):  
Linear Programming ◽  
Production Factors ◽  
Maximum Profit ◽  
Analysis Methods ◽  
Profit Analysis ◽  
Business Profit ◽  
Programming Analysis

The purpose of this study is to study the maximization of business profits through the determination of the optimal use of production factors in the trawl ring business in Latuhalat. Observations and direct interviews based on questionnaires were carried out on 3 fishermen who owned trawl ring businesses in 3 hamlets in Latuhalat. Data were analyzed using business profit analysis methods and linear programming analysis. The results showed that: 1). The optimal use of production factors for trawling by the respondents in Latuhalat is 41 - 45 trips in June using 15.2 - 21.1 liters of gasoline, 280.9 - 304.4 liters of gasoline, 10.1 - 21 , 2 liters of oil, using labor as much as 15-17 people for 8 hours / day, and using transportation and FAD services as much as 351.3 - 360 HOK or 8 hours per day; and 2). The maximum profit that can be obtained from trawling rings by respondents in Latuhalat is Rp. 165,545,500 - up to Rp. 184,800,000, - in June. To achieve this maximum profit, respondents need to produce 25,270.3 kg of fish to 27,540.98 kg in June.

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