• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2131-2153
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• 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.

• Journal of the Korean Society of Radiology
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• v.10 no.2
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• pp.81-87
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• 2016

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some fatty liver patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of fatty liver. As the results of examining over 30 example CT images of fatty liver, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including Average Gray Level, Entropy 96.67%, Skewness 93.33%, and Smoothness while others showed a little low disease recognition rate: 83.33% for Uniformity 86.67% and for Average Contrast 80%. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of fatty liver and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.17 no.9
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• pp.1006-1012
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• 2004

In this paper, neural networks is studied to apply as a PD source classification in XLPE power cable specimen. Two learning schemes are used to classification; BP(Back propagation algorithm), SOM(self organized map - kohonen network). As a PD source, using treeing discharge sources in the specimen, three defected models are made. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And a]so these distribution characteristics are applied to classify PD sources by two scheme of the neural networks. In conclusion, recognition efficiency of BP is superior to SOM.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.8
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• pp.71-80
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• 1998

A computer aided system has been developed for the focal length measurement/compensation in camera manufacture. Signal data proportional to light intensity is obtained and sampled very rapidly from the line CCD. Based on the measured signal, the MTF performance is calculated, where the MTF is the ratio of magnitude of the output image to the input image. In order to find the optimum MTF performance, an effcient algorithm has been implemented using the least squares technique. The developed system has been applied to a practical camera manufacturing process, and demonstrated high productivity with high precision.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1299-1302
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• 2005

Current mechanical devices are trending toward being a small size, high speedy, automation. For performing these functions, machinery elements organizing a machine should be designed exactly. Cams have high confidence and economics in ablility to transmit a motion. Accordingly, A cam mechanism is very important for processing the machine automatically. This paper introduce an inverse cam mechanism. A square shaped cam which cannot be commonly analyzed is designed and manufactured by using the NURBS interpolation algorithm. The objective of this paper is to develop a computer-aided design program. In this paper, a displacement curve of oscillating motion inverse cam mechanism with square shaped follower is analyzed. The data is redistibuted by the NURBS algorithm. A cam shape is designed by the instant velocity center method, and simulated to verify the validity of the operation state.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7A
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• pp.836-843
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• 2004

In per survivor processing (PSP) has a better performance than conventional phase offset estimators. But itsdefect is that it has a high complexity. In this paper, we propose the adaptive reduced state estimator (ARSE) algorithm not only to reduce the complexity, but also to have a good performance. The main principle of ARSE is changing the number of estimators dynamically during the decoding process according to the channel condition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7B
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• pp.886-891
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• 2011

Multicast has been a key routing service for efficient data dissemination in underwater acoustic sensor networks. In sensor networks, there are several multicast routing protocol which reflects sensor network nature. However, existing routing scheme was not targeted at underwater acoustic sensor networks which is hard to provide battery continually. Therefore, a specialized routing algorithm is essential for acoustic sensor networks. In this paper, we propose angle aided multicast routing algorithm for decreasing routing computation complexity, including virtual Euclidean Steiner point. Simulation results show better performance than exist routing Position Based Multicast, Geographic Multicast Routing. such as low computation capability and limited power consumption.

• Journal of Drive and Control
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• v.14 no.4
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• pp.29-36
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• 2017

Electronic control technologies that have long been developed for passenger cars spread to construction equipment and agricultural vehicles because of its outstanding performance achieved by embedded software. Especially, system program of transmission control unit (TCU) plays a crucial role for the superb shift quality, driving performance and fuel efficiency, etc. Since the control algorithm is embedded in software that is rarely analyzed, development of such a TCU cannot be conducted by conventional reverse engineering. Transmission simulator is a kind of electronic device that simulates the electric signals including driver operation command and output of various sensors installed in transmission. Standalone TCU can be run in normal operation mode with the signals provided by transmission simulator. In this research, transmission simulator for the tracked vehicle TCU is developed for the analysis of shift control algorithm from the experiments with standalone TCU. It was confirmed that shift experimental data for the simulator setup conditions can be used for the analysis of control algorithms on proportional solenoid valves and shift map.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.347-370
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• 2019

In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.