Texture Analysis and Genetic Algorithms for Osteoporosis Diagnosis

2019 ◽  
Vol 34 (05) ◽  
pp. 2057002
Author(s):  
Laatra Yousfi ◽  
Lotfi Houam ◽  
Abdelhani Boukrouche ◽  
Eric Lespessailles ◽  
Frédéric Ros ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Texture Analysis ◽  
Image Features ◽  
Diagnosis Of Osteoporosis ◽  
Occurrence Matrix ◽  
Two Parameters ◽  
And Control ◽  
Parameter Dependent ◽  
Two Populations ◽  
Distance Parameter

Early diagnosis of osteoporosis can efficiently predict fracture risk. There is a great demand to prevent this disease. The goal of this study was to distinguish osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis and genetic algorithms (GAs). Gray Level Co-occurrence Matrix (GLCM), Run length Matrix (RLM) and Binarized Statistical Image Features (BSIF) were used for texture analysis. Features are numerous and parameter-dependent. The related experts can pick out the useful input features for the classifier. It however remains a difficult task and may be inefficient or even harmful as the data pattern is not clear. In this paper, GAs were used to optimize the two parameters of the co-occurrence matrix (distance parameter or pixel separation, orientation or direction) and the number of gray levels used in the preprocessing quantification step. GAs were also used to select the best combination of features extracted from GLCM and RLM matrices. Experiments were conducted on two populations composed of Osteoporotic Patients and Control Subjects. Results show that GAs combined with GLCM and BSIF features can improve the classification rates (ACC = 87.50%) obtained using GLCM (ACC = 77.8%) alone.

Co-Occurrence Matrix-Based Statistical Model for Texture Analysis from Images

2011 ◽  
Vol 103 ◽  
pp. 717-724
Author(s):  
Hossain Shahera ◽  
Serikawa Seiichi
Keyword(s):  
Texture Analysis ◽  
Vision System ◽  
Ground Truth ◽  
Image Features ◽  
Image Texture ◽  
Gray Level ◽  
Machine Vision System ◽  
Statistical Features ◽  
Texture Surface ◽  
Occurrence Matrix

Texture surface analysis is very important for machine vision system. We explore Gray Level Co-occurrence Matrix-based 2ndorder statistical features to understand image texture surface. We employed several features on our ground-truth dataset to understand its nature; and later employed it in a building dataset. Based on our experimental results, we can conclude that these image features can be useful for texture analysis and related fields.

Designing Stability and Control Augmentation Systems Using Genetic Algorithms

2009 ◽  
Author(s):  
Ashraf Omran ◽  
Mohamed Elshabasy ◽  
Wael Mokhtar ◽  
Brett Newman ◽  
Mohamed Gharib
Keyword(s):  
Genetic Algorithms ◽  
Stability And Control ◽  
And Control

Genetic Diversity among Calamus brandisii Becc. (Arecaceae) Populations in Kerala Part of Western Ghats

2021 ◽  
Vol 43 (1) ◽  
pp. 38-42
Author(s):  
Kavungal Priya ◽  
◽  
Indira . ◽  
Vadakkethil Balakrishnan Sreekumar ◽  
Renuka . ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Western Ghats ◽  
Rapd Markers ◽  
Gene Diversity ◽  
Issr Markers ◽  
High Genetic Diversity ◽  
Western Ghats Of India ◽  
The Mean ◽  
Two Parameters ◽  
Two Populations

Calamus brandisii Becc. is one of the endemic slender rattans found in the Western Ghats of India. The genetic diversity of two main populations available in Kerala was investigated using 20 RAPD and 9 ISSR markers. Two parameters viz., gene diversity and genetic diversity within and among populations were analyzed. ISSR analysis showed quite high genetic diversity in Pandimotta compared to Bonacaud population whereas in RAPD markers both these populations were moderately diverse. The percentage of total genetic differentiation (Gst) among two populations is relatively higher than the mean Gst value indicating high genetic diversity within the populations. The genetic distance between these two populations was 0.1739 with ISSR markers and 0.1971 with RAPD markers. Because of its high genetic diversity, Pandimotta population can be treated as an important population of gene diversity with potentially useful genes. This may be included in the high priority reservoir for genetic conservation also.

scholarly journals HbA1c levels in individuals heterozygous for hemoglobin variants

2017 ◽  
Vol 63 (4) ◽  
pp. 341-346
Author(s):  
Ricardo Silva Tavares ◽  
Fábio Oliveira de Souza ◽  
Isabel Cristina Carvalho Medeiros Francescantonio ◽  
Weslley Carvalho Soares ◽  
Mauro Meira Mesquita
Keyword(s):  
Test Group ◽  
Exchange Chromatography ◽  
Group Method ◽  
Control Group ◽  
Significant Difference ◽  
Hemoglobin Variants ◽  
Different Populations ◽  
And Control ◽  
Two Populations ◽  
Hba1c Levels

Summary Objective: To evaluate the levels of glycated hemoglobin (HbA1c) in patients heterozygous for hemoglobin variants and compare the results of this test with those of a control group. Method: This was an experimental study based on the comparison of HbA1c tests in two different populations, with a test group represented by individuals heterozygous for hemoglobin variants (AS and AC) and a control group consisting of people with electrophoretic profile AA. The two populations were required to meet the following inclusion criteria: Normal levels of fasting glucose, hemoglobin, urea and triglycerides, bilirubin > 20 mg/dL and non-use of acetylsalicylic acid. 50 heterozygous subjects and 50 controls were evaluated between August 2013 and May 2014. The comparison of HbA1c levels between heterozygous individuals and control subjects was performed based on standard deviation, mean and G-Test. Results: The study assessed a test group and a control group, both with 39 adults and 11 children. The mean among heterozygous adults for HbA1c was 5.0%, while the control group showed a rate of 5.74%. Heterozygous children presented mean HbA1c at 5.11%, while the controls were at 5.78%. G-Test yielded p=0.93 for children and p=0.89 for adults. Conclusion: Our study evaluated HbA1c using ion exchange chromatography resins, and the patients heterozygous for hemoglobin variants showed no significant difference from the control group.

scholarly journals Improved H2 and H¥ conditions for robust analysis and control synthesis of linear systems

2005 ◽  
Vol 16 (4) ◽  
pp. 427-434 ◽  
Author(s):  
Alexandre Trofino ◽  
Daniel F. Coutinho ◽  
Karina A. Barbosa
Keyword(s):  
Linear Systems ◽  
Control Synthesis ◽  
H Infinity ◽  
Stability Performance ◽  
Augmented Space ◽  
Polytopic Uncertainties ◽  
Discrete Time Case ◽  
Performance Conditions ◽  
And Control ◽  
Parameter Dependent

This paper proposes improved H-2 and H-infinity conditions for continuous-time linear systems with polytopic uncertainties based on a recent result for the discrete-time case. Basically, the performance conditions are built on an augmented-space with additional multipliers resulting in a decoupling between the Lyapunov and system matrices. This nice property is used to develop new conditions for the robust stability, performance analysis, and control synthesis of linear systems using parameter dependent Lyapunov functions in a numerical tractable way.

scholarly journals FORMING THE STACK OF TEXTURE FEATURES FOR LIVER ULTRASOUND IMAGES CLASSIFICATION

2020 ◽  
Vol 3 (4) ◽  
pp. 240-251
Author(s):  
Dmitro Yuriiovych Hrishko ◽  
Ievgen Arnoldovich Nastenko ◽  
Maksym Oleksandrovych Honcharuk ◽  
Volodymyr Anatoliyovich Pavlov
Keyword(s):  
Random Forest ◽  
Texture Analysis ◽  
Binary Classification ◽  
Texture Features ◽  
Normal Liver ◽  
Group Method ◽  
Ultrasound Images ◽  
Liver Ultrasound ◽  
Random Forest Models ◽  
Occurrence Matrix

This article discusses the use of texture analysis methods to obtain informative features that describe the texture of liver ultrasound images. In total, 317 liver ultrasound images were analyzed, which were provided by the Institute of Nuclear Medicine and Radiation Diagnostics of NAMS of Ukraine. The images were taken by three different sensors (convex, linear, and linear sensor in increased signal level mode). Both images of patients with a normal liver condition and patients with specific liver disease (there were diseases such as: autoimmune hepatitis, Wilson's disease, hepatitis B and C, steatosis, and cirrhosis) were present in the database. Texture analysis was used for “Feature Construction”, which resulted in more than a hundred different informative features that made up a common stack. Among them, there are such features as: three authors’ patented features derived from the grey level co-occurrence matrix; features, obtained with the help of spatial sweep method (working by the principle of group method of data handling), which was applied to ultrasound images; statistical features, calculated on the images, brought to one scale with the help of differential horizontal and vertical matrices, which are proposed by the authors; greyscale pairs ensembles (found using the genetic algorithm), which identify liver pathology on images, transformed with the help of horizontal and vertical differentiations, in the best possible way. The resulting trait stack was used to solve the problem of binary classification (“norma-pathology”) of ultrasound liver images. A Machine Learning method, namely “Random Forest”, was used for this purpose. Before the classification, in order to obtain objective results, the total samples were divided into training (70 %), testing (20 %), and examining (10 %). The result was the best three Random Forest models separately for each sensor, which gave the following recognition rates: 93.4 % for the convex sensor, 92.9 % for the linear sensor, and 92 % for the reinforced linear sensor

scholarly journals WRITER IDENTIFICATION BY TEXTURE ANALYSIS BASED ON KANNADA HANDWRITING

2012 ◽  
pp. 80-85
Author(s):  
B.V. DHANDRA ◽  
VIJAYALAXMI.M. B ◽  
GURURAJ MUKARAMBI ◽  
MALLIKARJUN. HANGARGE
Keyword(s):  
Texture Analysis ◽  
Identification Problem ◽  
Document Image ◽  
Independent Method ◽  
Gray Level ◽  
Writer Identification ◽  
Texture Image ◽  
Major Research ◽  
Gabor Filtering ◽  
Occurrence Matrix

Writer identification problem is one of the important area of research due to its various applications and is a challenging task. The major research on writer identification is based on handwritten English documents with text independent and dependent. However, there is no significant work on identification of writers based on Kannada document. Hence, in this paper, we propose a text-independent method for off-line writer identification based on Kannada handwritten scripts. By observing each individual’s handwriting as a different texture image, a set of features based on Discrete Cosine Transform, Gabor filtering and gray level co-occurrence matrix, are extracted from preprocessed document image blocks. Experimental results demonstrate that the Gabor energy features are more potential than the DCTs and GLCMs based features for writer identification from 20 people.

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