• 대한의용생체공학회:의공학회지
• /
• 제21권2호
• /
• pp.137-144
• /
• 2000

Clustered microcalcifications in X-ray mammograms are an important sign for the diagnosis of breast cancer. A shape-based method, which is based on the morphological features of clustered microcalcifications, is proposed for classifying clustered microcalcifications into benign or malignant categories. To verify the effectiveness of the proposed shape features, clinical mammograms were used to compare the classification performance of the proposed shape features with those of conventional textural features, such as the spatial gray-leve dependence method and the wavelet-based method. Image features extracted from these methods were used as inputs to a three-layer backpropagation neural network classifier. The classification performance of features extracted by each method was studied by using receiver operating-characteristics analysis. The proposed shape features were shown to be superior to the conventional textural features with respect to classification accuracy.

• 스마트미디어저널
• /
• 제12권7호
• /
• pp.52-58
• /
• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

• 대한치과보철학회지
• /
• 제62권2호
• /
• pp.157-164
• /
• 2024

진단, 수술, 인상, 보철물 제작 등 임플란트 치료 전 과정에서 디지털 기술이 활용되고 있다. 본 증례에서는 디지털 수술 가이드를 이용하여 합병증 없이 상악동 거상술을 시행하고, 계획된 위치에 임플란트를 식립하였다. 골유착을 위한 치유 기간 이후, CAD-CAM(Computer-aided design/Computer-aided Manufacturing)으로 맞춤형 지대주 및 임시 보철물을 제작하여 장착하고, 환자의 적응도와 교합을 평가하였다. 임시 보철물상에서 교합 변화가 관찰되어, 광중합형 컴포지트 레진으로 수리하였다. 최종 보철물 제작 시, 지대주의 수직 침하, 임시 보철물의 형태와 적응된 교합 관계를 반영하기 위해, 이중 스캔과 지대주 수준의 디지털 인상을 채득하였다. 치은 압배 없이 치은 연하 변연을 인기하기 위해, CAD-CAM 소프트웨어상에서 라이브러리화된 지대주 데이터를 중첩하고, 지르코니아 최종보철물 제작하여 장착하였다. 구치부 임플란트 수복 시, 디지털 시스템을 이용하여 전통적인 방법에서 겪는 어려움을 줄이고, 수술부터 보철물 제작까지 효율적인 치료 과정과 안정적이고 예지성 있는 결과를 얻어 보고하는 바이다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권7호
• /
• pp.2131-2153
• /
• 2022

To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

• 대한방사선기술학회지:방사선기술과학
• /
• 제35권3호
• /
• pp.227-235
• /
• 2012

유방영상은 유방 전체의 재현 가능한 영상을 만들며, 만져지지 않는 조기 유방암의 가장 중요한 소견인 미세석회화와 종괴를 발견할 수 있어 유방 질환의 일차적인 선별검사로 이용되고 있다. 유방 병변의 미세석회화는 조기 유방암의 진단에 있어서 중요한 병변으로 보고되고 있지만 유방초음파 검사에서 검출이 어렵다. 본 연구에서는 유방초음파 영상에서 미세석회화 검출을 위해 6가지의 질감분석 파라미터를 이용하였으며, 정상 초음파영상과 미세석회화가 보이는 초음파 영상 간의 병변인식률을 알아보았다. 실험결과로는 유방촬영영상과 유방 초음파영상에서 병변을 구별하는 컴퓨터자동진단 인식률은 70~98%로 상당히 높은 결과를 나타내었다. ROC 분석에서도 평균대조도와 엔트로피 파라미터의 특이도는 다소 낮게 나타났으나, 나머지 4개 파라미터의 민감도와 특이도는 90% 이상을 나타내어 초음파영상에서 미세석회화 검출의 가능성을 보였다. 향후 6가지 질감분석 알고리즘들 외에 추가적인 파라미터 알고리즘의 연구가 계속 진행되어 컴퓨터자동진단의 실용화기반을 마련한다면 전문의 진단의 예비단계로서 더욱 중요한 의미를 가질 것이며, 유방암의 조기진단에 매우 유용할 것으로 사료된다.

• 대한의용생체공학회:의공학회지
• /
• 제42권3호
• /
• pp.71-79
• /
• 2021

Currently, the naked eyes-based diagnosis of bone metastases on CT images relies on qualitative assessment. For this reason, there is a great need for a state-of-the-art approach that can assess and follow-up the bone metastases with quantitative biomarker. Radiomics can be used as a biomarker for objective lesion assessment by extracting quantitative numerical values from digital medical images. In this study, therefore, we evaluated the clinical applicability of non-invasive and objective bone metastases computer-aided diagnosis using radiomics-based biomarkers in CT. We employed a total of 21 approaches consist of three-classifiers and seven-feature selection methods to predict bone metastases and select biomarkers. We extracted three-dimensional features from the CT that three groups consisted of osteoblastic, osteolytic, and normal-healthy vertebral bodies. For evaluation, we compared the prediction results of the classifiers with the medical staff's diagnosis results. As a result of the three-class-classification performance evaluation, we demonstrated that the combination of the random forest classifier and the sequential backward selection feature selection approach reached AUC of 0.74 on average. Moreover, we confirmed that 90-percentile, kurtosis, and energy were the features that contributed high in the classification of bone metastases in this approach. We expect that selected quantitative features will be helpful as biomarkers in improving the patient's survival and quality of life.

• Clinical Endoscopy
• /
• 제55권5호
• /
• pp.594-604
• /
• 2022

Over the past decade, technological advances in deep learning have led to the introduction of artificial intelligence (AI) in medical imaging. The most commonly used structure in image recognition is the convolutional neural network, which mimics the action of the human visual cortex. The applications of AI in gastrointestinal endoscopy are diverse. Computer-aided diagnosis has achieved remarkable outcomes with recent improvements in machine-learning techniques and advances in computer performance. Despite some hurdles, the implementation of AI-assisted clinical practice is expected to aid endoscopists in real-time decision-making. In this summary, we reviewed state-of-the-art AI in the field of gastrointestinal endoscopy and offered a practical guide for building a learning image dataset for algorithm development.

• 스마트미디어저널
• /
• 제5권1호
• /
• pp.49-54
• /
• 2016

Medical image processing is a compulsory process to diagnose many kinds of disease. Therefore, an automatic algorithm for this task is highly demanded as an important part to construct a computer-aided diagnosis system. In this paper, we introduce an automatic method to segment the liver region from 3D abdominal CT images using Otsu method. First, we choose a 2D slice which has most liver information from the whole 3D image. Secondly, on the chosen slice, we enhanced the image based on its intensity using Otsu method with multiple thresholds and use the threshold to enhance the whole 3D image. Then, we apply a liver mask to mark the candidate liver region. After that, we execute the Otsu method again to segment the liver region from the chosen slice and propagate the result to the whole 3D image. Finally, we apply preprocessing on the frontal side of 3D images to crop only the liver region from the image.

• Asian Pacific Journal of Cancer Prevention
• /
• 제14권1호
• /
• pp.97-102
• /
• 2013

Objective: The aim of this study was to develop an automated computer-aided diagnostic system for diagnosis of thyroid cancer pattern in fine needle aspiration cytology (FNAC) microscopic images with high degree of sensitivity and specificity using statistical texture features and a Support Vector Machine classifier (SVM). Materials and Methods: A training set of 40 benign and 40 malignant FNAC images and a testing set of 10 benign and 20 malignant FNAC images were used to perform the diagnosis of thyroid cancer. Initially, segmentation of region of interest (ROI) was performed by region-based morphology segmentation. The developed diagnostic system utilized statistical texture features derived from the segmented images using a Gabor filter bank at various wavelengths and angles. Finally, the SVM was used as a machine learning algorithm to identify benign and malignant states of thyroid nodules. Results: The SVMachieved a diagnostic accuracy of 96.7% with sensitivity and specificity of 95% and 100%, respectively, at a wavelength of 4 and an angle of 45. Conclusion: The results show that the diagnosis of thyroid cancer in FNAC images can be effectively performed using statistical texture information derived with Gabor filters in association with an SVM.

• 한국자동차공학회논문집
• /
• 제22권4호
• /
• pp.89-97
• /
• 2014

As the digital control technologies in automotive industry have advanced, electronic control units(ECUs) play a key-role to improve system performance. Transmission control unit(TCU) is a shifting controller for automatic transmission of which major functions are to determine the shift and manage the shifting process considering the various sensor signal on transmission and driver's commands. As with any ECU in vehicle, TCU performs complex algorithms such as shift control, diagnostic and failsafe functions. However, firmware design analysis is hardly possible by the reverse engineering due to code protection. Transmission simulator is a hardware-in-the-loop simulator which enables TCU to work in normal mode by simulating the electrical signal of TCU interface. In this research, diagnosis and failsafe algorithm implemented on commercialized TCU is analyzed by using the transmission simulator that is developed for wheel loader construction vehicle. This paper gives various experimental results on the proportional solenoid current trajectories for different operating modes, error detection criterion and limphome mode gears for all the possible cases of clutch malfunction. The derived results for conventional TCU can be applied to the development of inherent TCU algorithms and the transmission simulator can also be utilized for the test of TCU to be developed.