Segmentasi Paru-Paru pada Citra X-Ray Thorax Menggunakan Distance Regularized Levelset Evolution (DRLSE)

Hariyadi, Mokhamad Amin

doi:10.18860/mat.v9i1.4130

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Therefore, boundary-based active contours are gradually applied to image segmentation, and the most typical boundary model is the geodesic (GAC) model method. Although this method can be used to segment medical images, it is more complicated in numerical implementation and requires constant reinitialization, which is time-consuming [ 19 , 20 ]. The model is further improved in the traditional penalty term function, so that the penalty term diffusion rate tends to infinity, the diffusion rate is a bounded constant, and the level set segmentation accuracy is higher.…”

Section: Based On Deep Learning Medical Image Segmentation Tap and Cea Combined Detection Study On Thyroid Cancer Risk Prediction In Patimentioning

confidence: 99%

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

Lang

et al. 2021

Journal of Healthcare Engineering

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In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnosis due to their cheapness, no radiation, and low cost. The use of image processing technology to accurately segment the nodule area provides important auxiliary information for the doctor’s diagnosis, which is of great value for guiding clinical treatment. The purpose of this article is to explore the application value of combined detection of abnormal sugar-chain glycoprotein (TAP) and carcinoembryonic antigen (CEA) in the risk estimation of thyroid cancer in patients with thyroid nodules of type IV and above based on deep learning medical images. In this paper, ultrasound thyroid images are used as the research content, and the active contour level set method is used as the segmentation basis, and a segmentation algorithm for thyroid nodules is proposed. This paper takes ultrasound thyroid images as the research content, uses the active contour level set method as the basis of segmentation, and proposes an image segmentation algorithm Fast-SegNet based on deep learning, which extends the network model that was mainly used for thyroid medical image segmentation to more scenarios of the segmentation task. From January 2019 to October 2020, 400 patients with thyroid nodules of type IV and above were selected for physical examination and screening at the Health Management Center of our hospital, and they were diagnosed as thyroid cancer by pathological examination of thyroid nodules under B-ultrasound positioning. The detection rates of thyroid cancer in patients with thyroid nodules of type IV and above are compared; serum TAP and CEA levels are detected; PT-PCR is used to detect TTF-1, PTEN, and NIS expression; the detection, missed diagnosis, misdiagnosis rate, and diagnostic efficiency of the three detection methods are compared. This article uses the thyroid nodule region segmented based on deep learning medical images and compares experiments with CV model, LBF model, and DRLSE model. The experimental results show that the segmentation overlap rate of this method is as high as 98.4%, indicating that the algorithm proposed in this paper can more accurately extract the thyroid nodule area.

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Section: Based On Deep Learning Medical Image Segmentation Tap and Cea Combined Detection Study On Thyroid Cancer Risk Prediction In Patimentioning

confidence: 99%

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

Lang

et al. 2021

Journal of Healthcare Engineering

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“…The suprascapular nerve and suprascapular artery are distributed on the outside and base of the respiratory muscles, while the 4∼8 cm area inside the respiratory muscles is located in the middle of the respiratory muscles, which is far from the neurovascular distribution area, which can avoid nerve and blood vessel damage during muscle removal. Respiratory muscle training describes the use of respiratory muscle training to treat the posterior glenoid instability, and the use of cadaver specimens to verify the biomechanics of respiratory muscle training to treat the posterior glenoid defect can reconstruct the stability of the posterior glenoid [ 12 ]. However, there are few reports in the literature on whether the respiratory muscles have sufficient muscle mass to meet the needs of severe muscle Bankart muscle grafts [ 13 ].…”

Section: Meta-analysis and Ct Image Processing Of Respiratory Muscle Trainingmentioning

confidence: 99%

Multislice Spiral CT Image Analysis and Meta-Analysis of Inspiratory Muscle Training on Respiratory Muscle Function

Ming

et al. 2021

Journal of Healthcare Engineering

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Respiratory muscle function has a significant effect on stroke. Stroke is one of the most common cardiovascular and cerebrovascular diseases in the clinic and has a significant impact on the quality of life of patients. Hemiplegia, cerebral hemorrhage, and even death can occur, mainly in the elderly. In this paper, we meta-analyzed the effect of inspiratory muscle training on respiratory muscle function. In this article, we used a topic search method to search for relevant literature on respiratory muscle training and obtained 58 and 32 literature studies from CNKI and Wanfang Data, respectively. As a result of the screening, 36 and 28 documents were obtained. In this paper, 64 selected articles were studied. The authors make statistics on the literature of designing serum content index and multislice spiral CT (Member of the Society of Cardiological Technicians) image of patients, so as to analyze the influence of CT image and inspiratory muscle training on respiratory muscle function. The study showed that FVC, FEV1, MIP, and diaphragm mobility of the experimental group were significantly improved after treatment in more than 85% of the studies ( P < 0.05 ), while those of the control group were not significantly improved ( P > 0.05 ). The comparison between the two groups after treatment showed that FVC, FEV1, MIP, and diaphragm mobility of the experimental group were higher than those of the control group ( P < 0.05 ). The application of multislice spiral CT image analysis technology can effectively evaluate the effect of inspiratory muscle training on respiratory dysfunction in stroke patients, the mechanism of which regulates the expression of related pathways, suppresses the inflammatory response, and can reduce oxidative stress damage. Therefore, respiratory muscle training can improve the function of respiratory muscle and reduce the death rate of cerebellar hemorrhage in patients with stroke and other vascular diseases.

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“…Lalu pada penelitian ini akan dilakukan pengklasifikasian penyakit COVID-19 dengan mengimplementasikan teknik data mining, data mining merupakan penguraian pengetahuan yang terdapat pada basis data untuk memperoleh informasi yang pada prosesnya menggunakan teknik statistik, matematika, kecerdasan buatan, dan machine learning [12], salah satu teknik data mining yang akan digunakan yaitu metode k-nearest nighbor (K-NN) yang mana parameter untuk pengklasifikasian diperoleh dari nilai-nilai fitur ekstraksi metode gray level co-occurrence matrix (GLCM) yakni contrast, correlation, energy, dan homogeneity agar dapat mengidentifikasi penyakit COVID-19 berdasarkan citra radiologi x-ray thorax. Thorax itu sendiri merupakan bagian tubuh yang terletak di antara leher dan abdomen [13] yang berbentuk.kerucut yaitu lebih besar pada bagian bawah daripada bagian atasnya dan lebih panjang pada bagian belakang daripada depan, yang tersusun atas rangka tulang dada (Sternum), 12 ruas tulang belakang (Thoracalis), dan 12 pasang tulang rusuk [14]…”

Section: Pendahuluanunclassified

Klasifikasi K-NN dalam Identifikasi Penyakit COVID-19 Menggunakan Ekstraksi Fitur GLCM

Nafisah¹,

Adam²,

Carudin³

2021

JAIC

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Covid-19 merupakan penyakit yang sedang mewabah diberbagai belahan duni ae trmasuk Indonesia, penyakit ini menginfeksi saluran pernapasan yang disebabkan oleh jenis virus corona baru. Untuk mengetahui adanya virus ini diddalam tubuh dapat dilakukan pemeriksaan medis seperti cek darah, pemeriksaan radiologi rontgent (x-ray) dan swab. Oleh karena itu pda penelitian ini dilakukan identifikasi penyakit covid-19 berdasarkan citra rongen yang mana citra tersebut di ekstraksi menggunakan metode fitur ekstraksi GLCM yaitu contrast, correlation, energy, dan homogeneity, setelah didapat nilai dari ekstraksi lalu di klasifikasikan menggunakan metode klasifikasi data mining yaitu k-nearest neighbor dengan melakukan 3 pemodelan nilai inputan k. hasil yan diperoleh dari pengklasifikasian didapat akurasi sebesar 80%pada model 3 dengan nilai k = 5 dan pada model 1 dan 2 diperoleh hasil akurasi sebesar 90% dengan niali k = 1 dan k = 3.

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Segmentasi Paru-Paru pada Citra X-Ray Thorax Menggunakan Distance Regularized Levelset Evolution (DRLSE)

Cited by 3 publications

References 2 publications

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

Multislice Spiral CT Image Analysis and Meta-Analysis of Inspiratory Muscle Training on Respiratory Muscle Function

Klasifikasi K-NN dalam Identifikasi Penyakit COVID-19 Menggunakan Ekstraksi Fitur GLCM

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