Characterizing wavelet coefficients with decomposition for medical images

Eleiwy, Jabbar Abed

doi:10.54216/jisiot.020103

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…GLCM is obtained by counting the number of occurrences of pixel pairs in a certain direction and a distance of d in the image. The GLCM takes into account the number of pixels in the image, factors such as the grayscale relationship, spatial distance, and mutual direction [ 8 , 9 ]. Furthermore, the texture information can be represented by a relative frequency matrix.…”

Section: Brain Tumor Segmentation Methods Based On DL and Multimodal ...mentioning

confidence: 99%

3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images

Qian¹,

Xie²,

Xu³

2022

Emergency Medicine International

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Brain tumor segmentation is an important content in medical image processing, and it is also a very common research in medicine. Due to the development of modern technology, it is very valuable to use deep learning (DL) and multimodal MRI images to study brain tumor segmentation. In order to solve the problems of low efficiency and low accuracy of brain tumor segmentation, this paper proposes DL to conduct research on multimodal MRI image segmentation, aiming to make accurate diagnosis and treatment for doctors. In addition, this paper constructs an automatic diagnosis system for brain tumors, uses GLCM and discrete wavelet transform (DWT) to extract features from MRI images, and then uses a convolutional neural network (CNN) for final diagnosis; finally, through four. The comparison of the results between the two algorithms proves that the CNN algorithm has the better processing power and higher efficiency.

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Section: Brain Tumor Segmentation Methods Based On DL and Multimodal ...mentioning

confidence: 99%

3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images

Qian¹,

Xie²,

Xu³

2022

Emergency Medicine International

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“…Median filtering: the advantages are that the amount of computation is less, the operation is easier, and in practical applications, noise isolation smoothing is better than average filtering, and the boundaries of the image can be better protected [ 25 ]. The disadvantage is that during the filtering process, the image losses local information such as thin lines [ 26 ].…”

Section: Exploring Methods Of Nosocomial Infectionmentioning

confidence: 99%

[Retracted] Meta‐Resolve of Risk Factors for Nosocomial Infection in Patients Undergoing Thoracic Surgery

Chen

Zhang

Yang

2022

Contrast Media & Molecular Imaging

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As we all know, various complications may occur after surgery, and postoperative bleeding and infection are the most common in clinical practice. Postoperative infection mainly manifests as abdominal abscess, peritonitis, and fungal infection. Thoracic surgery is a very common clinical operation. It can directly deal with the relevant lesions, so a better curative effect can usually be obtained. However, patients undergoing thoracic surgery are generally more severely ill, with low immune resistance, long duration, and complicated surgical treatment process. Therefore, the probability of nosocomial infection is high, and there are many risk factors for infection. After the occurrence of HAI, it not only increases the suffering and economic burden of patients and the workload of medical staff but also prolongs the hospitalization time of patients, reduces the turnover rate of hospital beds, causes unnecessary economic losses, and affects the social and economic benefits of hospitals. Based on this, this paper proposes to analyze the risk factors of nosocomial infection in patients undergoing thoracic surgery, so as to provide a reference for the prevention or control of nosocomial infection. This paper analyzes the actual situation of nosocomial infection in a city hospital and then uses meta-analysis to determine the factors of nosocomial infection from the perspective of relevant research literature. Meta-analysis results show that patients older than 60 years have twice the risk of postoperative infection compared with patients younger than 60 years.

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“…Te criteria for selection of MS patients are certainty of MS diagnosis in accordance with clinical and paraclinic criteria and clear evidence for not associated with any other pathologies. Diagnosis criteria [13][14][15] that are accounted for in this work are as follows: certainty of MS, minimal two episodes of clinical manifestation of MS, clinical symptoms for two separately diferent lesions or clinical symptoms for one clinical lesion, and another subclinical lesion that is demonstrated by neurophysiologic evaluation or MRI. Te exclusion criteria of this research work are lack of all clinical information about the patient and the patients that do not agree to participate in the study.…”

Section: Methodsmentioning

confidence: 99%

Tensiomyography and Statistical Analysis Based Muscle Change Detection in Multiple Sclerosis for Smart Healthcare

Rusu

Neamţu

et al. 2022

Journal of Healthcare Engineering

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The impact of demyelinization on muscle fiber changes and the type of changes in multiple sclerosis (MS) is very hard to estimate. One of the major problems of MS patients is muscle fatigue and decrease of muscle force in the range of 16–57%. The objective of this research work is to estimate various aspects of muscle changes at tibial muscle (mTA) level using a noninvasive method named as tensiomyography (TMG). TMG provides information about muscle functions in MS. This study includes 40 MS patients among which 18 are males (45%) and 22 are females (55%). They are divided in two subgroups: subgroup A and subgroup B. Subgroup A includes 20 MS patients without clinical decelable gait disorders and subgroup B includes 20 MS patients with clinical decelable gait disorders. Also, we have a control group that includes 20 healthy people with the same average age. Average age is 38.15 ± 11.19 y for MS patients and 39.34 ± 10.57 for healthy people. Evaluation measures include ADL score and EDSS scale. The ADL score is 0 for patients from subgroup A and 1 for patients from subgroup B. The EDSS score is 1 for subgroup A and 2.5 for subgroup B. This study confirms the importance of TMG based evaluation of muscle changes in MS patients. This smart healthcare system is also used for prediction of the muscle changes and muscle imbalance. Contraction time (Tc) recordings are used to detect the muscle fatigue which is a specific symptom of MS. The value of Tc for subgroup A is 45.8 ms and subgroup B is 61.37 ms for right side. Analysis of these two parameters such as Dm and Tc could define the muscle behaviour and help provide early information about the possibility of developing gait disorders. This smart TMG system analyses the muscle tone in the best possible way to predict the onset of any diseases which is an integral part of the smart healthcare system.

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Characterizing wavelet coefficients with decomposition for medical images

Cited by 9 publications

References 4 publications

3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images

3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images

[Retracted] Meta‐Resolve of Risk Factors for Nosocomial Infection in Patients Undergoing Thoracic Surgery

Tensiomyography and Statistical Analysis Based Muscle Change Detection in Multiple Sclerosis for Smart Healthcare

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