• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2197-2204
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• 2015

In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1383-1391
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• 2011

Following the appearance of the latest medical equipment with improved function, the importance of image analysis which enables effective image processing and analysis consistent with the hardware performance is on the rise. As well as, ongoing study is being done on the 2D medical image processing and 3D reconstruction. This paper segments chest CT images into each stage and finally shows 3D reconstruction of each segmented result. Among various image segmentation methods, Region Growing and apply sharpening and Gamma Controller as for image improvement for effective segmentation, image segmentation in order of bronchus and lung, bronchus, lung. Human organs image of segmented is use VTK(Visualization Toolkit) to make 3D reconstruction, two and three-dimensional medical image processing and analysis for lesions diagnosis are able to utilized.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.371-378
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• 2000

In this study, we proposed pathological pattern detection system for X-ray chest image using artificial neural network. In a physical examination, radiologists have checked on the chest image projected the view box by a magnifying glass and found out what the disease is. Here, the detection of X-ray fluoroscopy is tedious and time-consuming for human doing. Lowering of efficiency for chest diagnosis is caused by lots mistakes of radiologist because of detecting the micro pathology from the film of small size. So, we proposed the method for disease detection using artificial neural network and digital image processing on a X-ray chest image. This method composes the function of image sampling, median filter, image equalizer used neural network and pattern recognition used neural network. We confirm this method has improved the problem of a conventional method.

• Progress in Medical Physics
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• v.31 no.3
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• pp.81-98
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• 2020

This review aims to provide a brief, comprehensive overview of advanced technologies of nuclear medicine physics, with a focus on recent developments from both hardware and software perspectives. Developments in image acquisition/reconstruction, especially the time-of-flight and point spread function, have potential advantages in the image signal-to-noise ratio and spatial resolution. Modern detector materials and devices (including lutetium oxyorthosilicate, cadmium zinc tellurium, and silicon photomultiplier) as well as modern nuclear medicine imaging systems (including positron emission tomography [PET]/computerized tomography [CT], whole-body PET, PET/magnetic resonance [MR], and digital PET) enable not only high-quality digital image acquisition, but also subsequent image processing, including image reconstruction and post-reconstruction methods. Moreover, theranostics in nuclear medicine extend the usefulness of nuclear medicine physics far more than quantitative image-based diagnosis, playing a key role in personalized/precision medicine by raising the importance of internal radiation dosimetry in nuclear medicine. Now that deep-learning-based image processing can be incorporated in nuclear medicine image acquisition/processing, the aforementioned fields of nuclear medicine physics face the new era of Industry 4.0. Ongoing technological developments in nuclear medicine physics are leading to enhanced image quality and decreased radiation exposure as well as quantitative and personalized healthcare.

• Korean Journal of Head & Neck Oncology
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• v.34 no.2
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• pp.29-34
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• 2018

Background/Objectives: The aim of this study was to investigate the diagnostic value of i-scan in the differential diagnosis of oral cavity leukoplakia based on visualization of abnormal vascular features. Materials & Methods: Thirty- one patients with oral cavity leukoplakia were enrolled in the study. Images of their oral cavity obtained using conventional white light endoscopy and an i-scan-enhanced endoscopy (Pentax DEFINA EPK-3000 Video Processors, with Pentax VNLJ10) were reviewed. The microvascular features of the lesions and vascular changes were analyzed and the results were compared with the histopathologic diagnosis. Results: Among the 31 oral cavity leukoplakia patients, 8 (25.8%) patients revealed hyperkeratosis, 10 (31.2%) low-grade dysplasia, 5 (16.2%) high-grade dysplasia and 8 (25.8%) invasive squamous cell carcinoma on histopathologic examination. Using i-scan-enhanced endoscopy, we could found abnormal vascular change with neoplastic neoangiogenesis in most high-grade dysplasia or invasive cancer in oral cavity. (high-grade dysplasia: 4/5 [80.0%], and invasive squamous cell carcinoma: 7/8 [87.5%]). Conclusion: i-scan-enhanced endoscopy could be a useful optical technique for the diagnosis of oral cavity leukoplakia. Our results suggest that i-scan may be a promising diagnostic tool in the early detection of suspected oral mucosal lesion.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.635-643
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• 2021

The results of empirical researches on the diagnosis of lung cancer are insufficient, so it is limited to objectively judge the clinical possibility and utilization according to the accuracy of diagnosis. Thus, this study retrospectively analyzed the lung cancer diagnostic performance of PET-MRI (Positron Emission Tomography-Magnetic Resonance Imaging) by using the decision matrix. This study selected and experimented total 165 patients who received both hematological CEA (Carcinoembryonic Antigen) test and hybrid PET-MRI (¹⁸F-FDG, 5.18 MBq/kg / Body TIM coil. VIVE-Dixon). After setting up the result of CEA (positive:>4 ㎍/ℓ. negative:<2.5㎍/ℓ) as golden data, the lung cancer was found in the image of PET-MRI, and then the SUV_max (positive:>4, negative:<1.5) was measured, and then evaluated the correlation and significance of results of relative diagnostic performance of PET-MRI compared to CEA through the statistical verification (t-test, P>0.05). Through this, the PET-MRI was analyzed as 96.29% of sensitivity, 95.23% of specificity, 3.70% of false negative rate, 4.76% of false positive rate, and 95.75% of accuracy. The false negative rate was 1.06% lower than the false positive rate. The PET-MRI that significant accuracy of diagnosis through high sensitivity and specificity, and low false negative rate and false positive rate of lung cancer, could acquire the fusion image of specialized soft tissue by combining the radio-pharmaceuticals with various sequences, so its clinical value and usefulness are regarded as latently sufficient.

• Applied Microscopy
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• v.51
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• pp.6.1-6.9
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• 2021

Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different (p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promising statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p < 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.

• Imaging Science in Dentistry
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• v.53 no.1
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• pp.61-67
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• 2023

Purpose: The aim of this study was to objectively detect simulated tooth ankylosis using a novel method involving cone-beam computed tomography (CBCT). Materials and Methods: Tooth ankylosis was simulated in single-rooted human permanent teeth, and CBCT scans were acquired at different current levels (5, 6.3, and 8 mA) and voxel sizes (0.08, 0.125, and 0.2). In axial reconstructions, a line of interest was perpendicularly placed over the periodontal ligament space of 21 ankylosed and 21 non-ankylosed regions, and the CBCT grey values of all voxels along the line of interest were plotted against their corresponding X-coordinates through a line graph to generate a profile. The image contrast was increased by 30% and 60% and the profile assessment was repeated. The internal area of the resulting parabolas was obtained from all images and compared between ankylosed and non-ankylosed regions under different contrast enhancement conditions, voxel sizes, and mA levels using multi-way analysis of variance with the Tukey post hoc test(α=0.05). Results: The internal area of the parabolas of all non-ankylosed regions was significantly higher than that of the ankylosed regions(P<0.05). Contrast enhancement led to a significantly greater internal area of the parabolas of non-ankylosed regions (P<0.05). Overall, voxel size and mA did not significantly influence the internal area of the parabolas(P>0.05). Conclusion: The proposed novel method revealed a relevant degree of applicability in the detection of simulated tooth ankylosis; increased image contrast led to greater detectability.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.801-807
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• 2024

Linear motion robots are devices that perform functions such as transferring parts or positioning devices, and require high precision. In companies that develop linear robot application systems, human workers are in charge of quality control and fault diagnosis of linear robots, and the result and accuracy of a fault diagnosis varies depending on the skill level of the person in charge. Recently, there have been many attempts to utilize artificial intelligence to diagnose faults in industrial devices. In this paper, we present a system that automatically diagnoses linear rail and ball screw misalignment of a linear robot using transfer learning. In industrial systems, it is difficult to obtain a lot of learning data, and this causes a data imbalance problem. In this case, a transfer learning model configured by retraining an established model is widely used. The information obtained by using an acceleration sensor and torque sensor was used, and its usefulness was evaluated for each case. After converting the signal obtained from the sensor into a spectrogram image, the type of abnormality was diagnosed using an image recognition artificial intelligence classifier. It is expected that the proposed method can be used not only for linear robots but also for diagnosing other industrial robots.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1528-1535
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• 1997

Many radiological modalities has been applied to medicine as a basic fundamental diagnosis and therapy recently. The prevalence of computer systems affect most images to be digitized. However conventional X-ray film images are not digital images eventhough they covers 70% of all radiologica images. This is the hinderacne of building PACS. In this paper all radiological digital imaging parts such as DSA. CR. MRI. SPECT. PET and ultrasonography were briefly introduced and the applications were described. In brief digital radiography contribute to enhance the medical service quality. And the digital substituition of conventional X-ray film image is inevitable.