Nondestructive Diagnosis and Analysis of Computed Microtomography Images via Texture Descriptors

Fernandes, Sandro R.; Assis, Joaquim Teixeira de; Estrela, Vania V.; Razmjooy, Navid; Deshpande, Anand; Patavardhan, Prashant P.; Aroma, R. Jenice; Raimond, Kumudha; Loschi, Hermes; Nascimento, Douglas

doi:10.1007/978-3-030-57552-6_16

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…(3) Method 3 : U-Net . Convolutional CNNs used for the classification are composed of convolutional layers followed by several fully connected layers that map the feature image produced by kernels into a fixed-size feature vector [ 72 ]. Nevertheless, one of the defects of CNN is that each time a convolutional operation is accomplished for the classification, feature map resolution is diminished by half.…”

Section: Discussionmentioning

confidence: 99%

Osteolysis: A Literature Review of Basic Science and Potential Computer‐Based Image Processing Detection Methods

Saadi

Ranjbarzadeh

kazemi³

et al. 2021

Computational Intelligence and Neuroscience

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Osteolysis is one of the most prominent reasons of revision surgeries in total joint arthroplasty. This biological phenomenon is induced by wear particles and corrosion products that stimulate inflammatory biological response of surrounding tissues. The eventual responses of osteolysis are the activation of macrophages leading to bone resorption and prosthesis failure. Various factors are involved in the initiation of osteolysis from biological issues, design, material specifications, and model of the prosthesis to the health condition of the patient. Nevertheless, the factors leading to osteolysis are sometimes preventable. Changes in implant design and polyethylene manufacturing are striving to improve overall wear. Osteolysis is clinically asymptomatic and can be diagnosed and analyzed during follow-up sessions through various imaging modalities and methods, such as serial radiographic, CT scan, MRI, and image processing-based methods, especially with the use of artificial neural network algorithms. Deep learning algorithms with a variety of neural network structures such as CNN, U-Net, and Seg-UNet have proved to be efficient algorithms for medical image processing specifically in the field of orthopedics for the detection and segmentation of tumors. These deep learning algorithms can effectively detect and analyze osteolytic lesions well in advance during follow-up sessions in order to administer proper treatments before reaching a critical point. Osteolysis can be treated surgically or nonsurgically with medications. However, revision surgeries are the only solution for the progressive osteolysis. In this literature review, the underlying causes, mechanisms, and treatments of osteolysis are discussed with the main focus on the possible computer-based methods and algorithms that can be effectively employed for the detection of osteolysis.

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Section: Discussionmentioning

confidence: 99%

Osteolysis: A Literature Review of Basic Science and Potential Computer‐Based Image Processing Detection Methods

Saadi

Ranjbarzadeh

kazemi³

et al. 2021

Computational Intelligence and Neuroscience

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show abstract

“…The Henry gas solubility optimizer has three principal managing variables:

{k}_{i,j}

, γ, and F . (1)

{k}_{i,j}

indicates the

i{\rm{th}}\

gas solubility in set j and is based on the time of repetition [32, 33]. Therefore, the exploration factors are transferred from the global to the local stage in addition to transferring to the finest location; thus, this gains the most suitable equilibrium among exploitation and exploration characteristics.…”

Section: Converged Henry Gas Solubility Optimization Algorithmmentioning

confidence: 99%

An innovative technique for optimization and sensitivity analysis of a PV/DG/BESS based on converged Henry gas solubility optimizer: A case study

Ghadimi

Sedaghat

Azar

et al. 2023

IET Generation Trans & Dist

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The construction of hybrid power plants with renewable resources can bring significant economic benefits if it is evaluated economically and technically. The present study uses a novel optimum methodology for designing a combined solar/battery/diesel system in Yarkant, Xinjiang Uyghur Autonomous Region of China. In the desired system, the green energy combined system is designed to reduce the use of diesel generators. The diesel generator has been used in the photovoltaic, diesel, and battery to support green energy resources and batteries, as well as function as a backup generator for critical times whenever the production of green energy resources is low or the load demand is high. The amount of CO2 emitted, the probability of load shortage and the system cost on yearly basis are the major goals in the process of optimization. Here, the single‐objective problem is created by using the ε‐constraint technique to combine the many objectives. An improved Henry gas solubility optimizer handles the problem of optimization. To demonstrate the superiority of the strategy, a comparison is conducted between the simulation outcomes of the offered system, HOMER, and particle swarm optimizer ‐based optimum systems from the literature. The sensitivity of each parameter is also examined using sensitivity analysis.

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“…The concern arises from multiple circumstances, such as the poor quality of measuring instruments and cameras, the low degree of the user interface in photography, the environmental factors, and the prevalence of noise. Sometimes, any crucial details from the images disappear from the above examples, rendering the processing too complex [ 19 ]. Contrast enhancement is a method for addressing the problems of contrast consistency.…”

Section: Skin Cancer Preprocessingmentioning

confidence: 99%

Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

Shi

Hamian

2021

Computational Intelligence and Neuroscience

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Melanoma is defined as a disease that has been incurable in advanced stages, which shows the vital importance of timely diagnosis and treatment. To diagnose this type of cancer early, various methods and equipment have been used, almost all of which required a visit to the doctor and were not available to the public. In this study, an automated and accurate process to differentiate between benign skin pigmented lesions and malignant melanoma is presented, so that it can be used by the general public, and it does not require special equipment and special conditions in imaging. In this study, after preprocessing of the input images, the region of interest is segmented based on the Otsu method. Then, a new feature extraction is implemented on the segmented image to mine the beneficial characteristics. The process is then finalized by using an optimized Deep Believe Network (DBN) for categorization into 2 classes of normal and melanoma cases. The optimization process in DBN has been performed by a developed version of the newly introduced Thermal Exchange Optimization (dTEO) algorithm to obtain higher efficacy in different terms. To show the method’s superiority, its performance is compared with 7 different techniques from the literature.

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Nondestructive Diagnosis and Analysis of Computed Microtomography Images via Texture Descriptors

Cited by 5 publications

References 23 publications

Osteolysis: A Literature Review of Basic Science and Potential Computer‐Based Image Processing Detection Methods

Osteolysis: A Literature Review of Basic Science and Potential Computer‐Based Image Processing Detection Methods

An innovative technique for optimization and sensitivity analysis of a PV/DG/BESS based on converged Henry gas solubility optimizer: A case study

Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

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