• Journal of the Korea Society of Computer and Information
• /
• v.27 no.9
• /
• pp.1-12
• /
• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.1
• /
• pp.113-120
• /
• 2018

Angiography and CT angiography are used widely for the examination of vascular diseases, but the diagnosis of such diseases is made mostly by the subjective judgment of the inspector. This paper proposes a method for detecting the suspicious regions of stenosis and aneurysm in the inner surfaces of 3D blood vessel models reconstructed from medical images. Initially, the 3D curve-skeletons of the blood vessel models and the contours at the nodes of the curve-skeletons were generated. Next, the 3D curve-skeletons were divided into a set of branches and the areas of normal contours of nodes located in each branch were calculated. The nodes whose contours contain suspicious regions were detected by taking into account the average area, maximum and minimum areas, and the area difference between the adjacent normal contours. The diagnosis of stenosis and aneurysm can be supported by properly visualizing the suspicious regions detected. The suspicious regions of the disease were identified by implementing and testing it using several data sets of human blood vessels, highlighting the usefulness of the proposed method.

• Progress in Medical Physics
• /
• v.23 no.3
• /
• pp.199-208
• /
• 2012

The computational human phantom including major radiation sensitive organs at risk (OARs) can be used in the field of radiotherapy, such as the variation of secondary cancer risks caused by the radiation therapy and the effective dose evaluation in diagnostic radiology. The present study developed a Korean adult female voxel phantom, VKH-Woman, based on serially sectioned color slice images of Korean female cadaver. The height and weight of the developed female voxel phantom are 160 cm and 52.72 kg, respectively that are virtually close to those of reference Korean female (161 cm and 54 kg). The female phantom consists of a total of 39 organs, including 27 organs recommended in the ICRP 103 publication for the effective dose calculations. The female phantom composes of $261{\times}109{\times}825$ voxels (=23,470,425 voxels) and the voxel resolution is $1.976{\times}1.976{\times}2.0619mm^3$ in the x, y, and z directions. The VHK-Woman is provided as both ASCII and Binary data formats to be conveniently implemented in Monte Carlo codes.

• Journal of Korea Multimedia Society
• /
• v.13 no.7
• /
• pp.1044-1050
• /
• 2010

Endoscopic ultrasonography is a medical procedure in endoscopy combined with ultrasound to obtain images of the internal organs. It is useful to have a predictive pathological manifestation since a doctor can observe tumors under mucosa. However, it is often subjective to judge the degree of malignant degeneration of tumors. Thus, in this paper, we propose a feature analysis procedure to make the pathological manifestation more objective so as to improve the accuracy and recall of the diagnosis. In the process, we extract the ultrasound region from the image obtained by endoscopic ultrasonography. It is necessary to standardize the intensity of this region with the intensity of water region as a base since frequently found small intensity difference is only to be inefficient in the analysis. Then, we analyze the spot region with high echo and calcium deposited region by applying LVQ algorithm and bit plane partitioning procedure to tumor regions selected by medical expert. For detailed analysis, features such as intensity value, intensity information included within two random points chosen by medical expert in tumor region, and the slant of outline of tumor region in order to decide the degree of malignant degeneration. Such procedure is proven to be helpful for medical experts in tumor analysis.

• Proceedings of the Korea Contents Association Conference
• /
• 2009.05a
• /
• pp.390-397
• /
• 2009

Endoscopic ultrasonography is a medical procedure in endoscopy combined with ultrasound to obtain images of the internal organs. It is useful to have a predictive pathological manifestation since a doctor can observe tumors under mucosa. However, it is often subjective to judge the degree of malignant degeneration of tumors. Thus, in this paper, we propose a feature analysis procedure to make the pathological manifestation more objective so as to improve the accuracy and recall of the diagnosis. In the process, we extract the ultrasound region from the image obtained by endoscopic ultrasonography. It is necessary to standardize the intensity of this region with the intensity of water region as a base since frequently found small intensity difference is only to be inefficient in the analysis. Then, we analyze the spot region with high echo and calcium deposited region by applying LVQ algorithm and bit plane partitioning procedure to tumor regions selected by medical expert. For detailed analysis, features such as intensity value, intensity information included within two random points chosen by medical expert in tumor region, and the slant of outline of tumor region in order to decide the degree of malignant degeneration. Such procedure is proven to be helpful for medical experts in tumor analysis.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.48 no.4
• /
• pp.24-31
• /
• 2011

A high data rate Medical Implant Communications Service (MICS) transmitter for implantable medical devices (IMD) is proposed. An orthogonal frequency division multiplexing (OFDM)-based multicarrier scheme is used to overcome the data rate limitation caused by the narrow bandwidth of 300 kHz. The proposed transmitter utilizes multiple MICS channels simultaneously, supporting increased data rate. To satisfy the MICS regulation, various schemes are applied including optimized subcarrier allocation and inverse fast Fourier transform (IFFT) architecture, and additional sidelobe suppression technique. Simulation results show that the proposed transmitter can support a maximum data rate of 4.86 Mbps, which is more than ten times faster than the previous systems.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.11
• /
• pp.1437-1444
• /
• 2020

With the development of computer vision systems, many advances have been made in the fields of surveillance, biometrics, medical imaging, and autonomous driving. In the field of autonomous driving, in particular, the object detection technique using deep learning are widely used, and the paved road detection is a particularly crucial problem. Unlike the ROI detection algorithm used in general object detection, the structure of paved road in the image is heterogeneous, so the ROI-based object recognition architecture is not available. In this paper, we propose a deep neural network architecture for atypical paved road detection using Semantic segmentation network. In addition, we introduce the multi-scale semantic segmentation network, which is a network architecture specialized to the paved road detection. We demonstrate that the performance is significantly improved by the proposed method.