• Clinical Endoscopy
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• v.56 no.5
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• pp.553-562
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• 2023

Colonoscopy plays an important role in reducing the incidence and mortality of colorectal cancer by detecting adenomas and other precancerous lesions. Image-enhanced endoscopy (IEE) increases lesion visibility by enhancing the microstructure, blood vessels, and mucosal surface color, resulting in the detection of colorectal lesions. In recent years, various IEE techniques have been used in clinical practice, each with its unique characteristics. Numerous studies have reported the effectiveness of IEE in the detection of colorectal lesions. IEEs can be divided into two broad categories according to the nature of the image: images constructed using narrow-band wavelength light, such as narrow-band imaging and blue laser imaging/blue light imaging, or color images based on white light, such as linked color imaging, texture and color enhancement imaging, and i-scan. Conversely, artificial intelligence (AI) systems, such as computer-aided diagnosis systems, have recently been developed to assist endoscopists in detecting colorectal lesions during colonoscopy. To gain a better understanding of the features of each IEE, this review presents the effectiveness of each type of IEE and their combination with AI for colorectal lesion detection by referencing the latest research data.

• Smart Media Journal
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• v.13 no.10
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• pp.28-34
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• 2024

Alzheimer's disease is a form of dementia that can be managed by identifying the disease in its initial phases. In recent times, numerous computer-aided diagnostic techniques utilizing magnetic resonance imaging (MRI) have demonstrated promising outcomes in the categorization of Alzheimer's disease (AD). The OASIS MRI dataset was utilized which has 80,000 brain MRI images. It is suggested to resample this dataset as it is highly imbalanced and posed a challenge in preventing bias toward majority class while employing the convolution neural network (CNN) model for classification. This paper examines and extracts patterns and features of 461 patients taken from the OASIS dataset. The research has aimed at utilizing the Base Model of EfficientNetV2B0 with custom classification layers, and simplified custom CNN model, also exploring Multi-class classification across four distinct classes: Non-Demented, Very Mild Demented, Mild Demented, Moderate Demented in addition to binary classification as Non-Demented and treating other classes as demented. Furthermore, different dataset sizes were experimented with 5,000 and 20,000 for each class to be discussed in this paper. The experiment results indicate that EfficientNetV2B0 achieved the accuracy of 98% in binary classification, 99% in multiclass. Whereas custom sequential CNN model in multiclass classification presents the accuracy of 96% for 20,000 dataset size and 98% for 80,000 dataset size.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.940-952
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• 2020

The Alzheimer's disease (AD) is a neurodegenerative disease commonly found in the elderly individuals. It is one of the most common forms of dementia; patients with AD suffer from a degradation of cognitive abilities over time. To correctly diagnose AD, compuated-aided system equipped with automatic classification algorithm is of great importance. In this paper, we propose a novel deep learning based classification algorithm that takes advantage of MRI biomarker images including brain areas of hippocampus and cerebrospinal fluid for the purpose of improving the AD classification performance. In particular, we develop a new approach that effectively applies MRI biomarker patch images as input to 3D Deep Convolution Neural Network. To integrate multiple classification results from multiple biomarker patch images, we proposed the effective confidence score fusion that combine classification scores generated from soft-max layer. Experimental results show that AD classification performance can be considerably enhanced by using our proposed approach. Compared to the conventional AD classification approach relying on entire MRI input, our proposed method can improve AD classification performance of up to 10.57% thanks to using biomarker patch images. Moreover, the proposed method can attain better or comparable AD classification performances, compared to state-of-the-art methods.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.151-160
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• 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.4
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• pp.338-343
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• 2009

Tomography is the imaging method of cross sectional area using multi beam signals and is mainly applied to the medical diagnosis to acquire the image of the inside human body. This method is pretty meaningful in nondestructive evaluation field since the imaging of the inspection region can enhance the comprehension of the inspector. Recently, much attention has been paid to the guided wave for the diagnosis of platelike structures. So, in this work, a study on the imaging of the damage location in a plate was carried out on the basis of computer aided analysis of guided waves and tomographic imaging. To this end, boundary element method was employed to analyze the effect of the damage in plate on the propagation of the guided waves and the analytic results were applied to the tomographic imaging method to identify the damage location. Consequently, it was shown that the number of sensors heavily affect the inspection performance of the damage location.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.829-840
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• 2013

In this paper, we propose a new lung segmentation method for chest x-ray images which can take both global and local properties into account. Firstly, the initial lung segmentation is computed by applying the active shape model (ASM) which keeps the shape of deformable model from the pre-learned model and searches the image boundaries. At the second segmentation stage, we also applied the localizing region-based active contour model (LRACM) for correcting various regional errors in the initial segmentation. Finally, to measure the similarities, we calculated the Dice coefficient of the segmented area using each semiautomatic method with the result of the manually segmented area by a radiologist. The comparison experiments were performed using 5 lung x-ray images. In our experiment, the Dice coefficient with manually segmented area was $95.33%{\pm}0.93%$ for the proposed method. Effective segmentation methods will be essential for the development of computer-aided diagnosis systems for a more accurate early diagnosis and prognosis regarding lung cancer in chest x-ray images.

• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.149-161
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• 2022

Purpose To investigate the correlation between computer-aided diagnosis (CAD) parameters in 3-tesla (T) MRI and pathologic immunohistochemical (IHC) markers in invasive carcinoma of no special type (NST). Materials and Methods A total of 94 female who were diagnosed with NST carcinoma and underwent 3T MRI using CAD, from January 2018 to April 2019, were included. The relationship between angiovolume, curve peak, and early and late profiles of dynamic enhancement from CAD with pathologic IHC markers and molecular subtypes were retrospectively investigated using Dwass, Steel, Critchlow-Fligner multiple comparison analysis, and univariate binary logistic regression analysis. Results In NST carcinoma, a higher angiovolume was observed in tumors of higher nuclear and histologic grades and in lymph node (LN) (+), estrogen receptor (ER) (-), progesterone receptor (PR) (-), human epidermal growth factor 2 (HER2) (+), and Ki-67 (+) tumors. A high rate of delayed washout and a low rate of delayed persistence were observed in Ki-67 (+) tumors. In the binary logistic regression analysis of NST carcinoma, a high angiovolume was significantly associated with a high nuclear and histologic grade, LN (+), ER (-), PR (-), HER2 (+) status, and non-luminal subtypes. A high rate of washout and a low rate of persistence were also significantly correlated with the Ki-67 (+) status. Conclusion Angiovolume and delayed washout/persistent rate from CAD parameters in contrast enhanced breast MRI correlated with predictive IHC markers. These results suggest that CAD parameters could be used as clinical prognostic, predictive factors.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.2
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• pp.222-230
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• 2022

Digital technology is gradually expanding its field and has a great influence on various fields of dentistry. Recently in digital dentistry, the importance of superimposing various 3-dimensional (3D) image data is emerging, in order to utilize gathered data effectively for diagnosis and prosthesis fabrication. Integrating data from facial scans, intraoral scans, and mandibular movement recordings can create a virtual patient. A virtual patient is formed by integrating digital 3D diagnostic data such as intraoral and extraoral soft tissues, residual dentition, and dynamic occlusion, and the results of prosthetic treatment can be evaluated virtually. The patients in this case report were a 37-year-old female whose chief complaint is that the appearance of the existing prosthesis was distorted and a 55-year-old female patient whose anterior prosthesis needed to be refabricated after the endodontic treatment. 3D facial scans were obtained from each patient, and the patient's mandibular movements were recorded using ARCUS Digma 2 (KaVo Dental GmbH, Biberach an der Riss, Germany). The collected data were integrated on computer-aided design (CAD) software (Exocad dental CAD; exocad GmbH, Darmstadt, Germany) and transferred to a virtual articulator to create a digital virtual patient. The temporary fixed prostheses were designed, restored, and evaluated, and it was reflected into the final restorations. With the aid of the virtual dental patient, accuracy and predictability could be increased throughout treatment, simplifying the occlusal adjustment and clinical evaluation with improved esthetic outcomes.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.3
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• pp.263-275
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• 2022

The loss of posterior occlusal support leads to further complications such as collapsed occlusal plane and reduced vertical dimension, and it may cause problems such as facial appearance change, reduced chewing efficiency, and temporomandibular joint disorders. In such case, it is necessary to re-establish occlusal plane and vertical dimension properly through accurate diagnosis and predictable treatment plan. This case report presents a 71-year-old female, whose occlusal plane was collapsed and posterior restorative space was insufficient. To perform a patient-friendly full mouth rehabilitation, proper vertical dimension and occlusal plane were decided by evaluation of interocclusal space at her physiologic mandibular rest position, swallowing, pronunciation, facial appearance, and the average length of anterior teeth. And then, the fixed provisional restorations were fabricated with the new occlusal position, and evaluated for 5 months with checking adaptation of masticatory muscles and any kind of clinical symptoms occurs or not. After confirmation of functional stability and esthetic satisfaction with the newly established occlusion, final definitive restorations were fabricated and inserted in the mouth. Through the above process, the treatment result was functionally and aesthetically satisfactory.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.1
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• pp.36-42
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• 2009

We have developed software for converting the volumetric voxel data obtained from X-ray computed tomography(CT) into computer-aided design(CAD) data. The developed software can used for non-destructive testing and evaluation, reverse engineering, and rapid prototyping, etc. The main algorithms employed in the software are image reconstruction, volume rendering, segmentation, and mesh data generation. The feasibility of the developed software is demonstrated with the CT data of human maxilla and mandible bones.