• Title/Summary/Keyword: Dice coefficient

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Application Feasibility Study of Non-local Means Algorithm in a Miniaturized Vein Near-infrared Imaging System (정맥 관찰용 소형 근적외선 영상 시스템에서의 비지역적평균 알고리즘 적용 가능성 연구)

  • Hyun-Woo Jeong;Youngjin Lee
    • Journal of the Korean Society of Radiology
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    • v.17 no.5
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    • pp.679-684
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    • 2023
  • Venous puncture is widely used to obtain blood samples for pathological examination. Because the invasive venipuncture method using a needle is repeatedly performed, the pain suffered by the patient increases, so our research team pre-developed a miniaturized near-infrared (NIR) imaging system in advance. To improve the image quality of the acquired NIR images, this study aims to model the non-local means (NLM) algorithm, which is well known to be efficient in noise reduction, and analyze its applicability in the system. The developed NIR imaging system is based on the principle that infrared rays pass through dichroic and long-pass filters and are detected by a CMOS sensor module. The proposed NLM algorithm is modeled based on the principle of replacing the pixel from which noise is to be removed with a value that reflects the distances between surrounding pixels. After acquiring an NIR image with a central wavelength of 850 nm, the NLM algorithm was applied to segment the final vein area through histogram equalization. As a result, the coefficient of variation of the NIR image of the vein using the NLM algorithm was 0.247 on average, which was an excellent result compared to conventional filtering methods. In addition, the dice similarity coefficient value of the NLM algorithm was improved by 62.91 and 9.40%, respectively, compared to the median filter and total variation methods. In conclusion, we demonstrated that the NLM algorithm can acquire accurate segmentation of veins acquired with a NIR imaging system.

Exploration of Hierarchical Techniques for Clustering Korean Author Names (한글 저자명 군집화를 위한 계층적 기법 비교)

  • Kang, In-Su
    • Journal of Information Management
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    • v.40 no.2
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    • pp.95-115
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    • 2009
  • Author resolution is to disambiguate same-name author occurrences into real individuals. For this, pair-wise author similarities are computed for author name entities, and then clustering is performed. So far, many studies have employed hierarchical clustering techniques for author disambiguation. However, various hierarchical clustering methods have not been sufficiently investigated. This study covers an empirical evaluation and analysis of hierarchical clustering applied to Korean author resolution, using multiple distance functions such as Dice coefficient, Cosine similarity, Euclidean distance, Jaccard coefficient, Pearson correlation coefficient.

Tumor Habitat Analysis Using Longitudinal Physiological MRI to Predict Tumor Recurrence After Stereotactic Radiosurgery for Brain Metastasis

  • Da Hyun Lee;Ji Eun Park;NakYoung Kim;Seo Young Park;Young-Hoon Kim;Young Hyun Cho;Jeong Hoon Kim;Ho Sung Kim
    • Korean Journal of Radiology
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    • v.24 no.3
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    • pp.235-246
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    • 2023
  • Objective: It is difficult to predict the treatment response of tissue after stereotactic radiosurgery (SRS) because radiation necrosis (RN) and tumor recurrence can coexist. Our study aimed to predict tumor recurrence, including the recurrence site, after SRS of brain metastasis by performing a longitudinal tumor habitat analysis. Materials and Methods: Two consecutive multiparametric MRI examinations were performed for 83 adults (mean age, 59.0 years; range, 27-82 years; 44 male and 39 female) with 103 SRS-treated brain metastases. Tumor habitats based on contrast-enhanced T1- and T2-weighted images (structural habitats) and those based on the apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) images (physiological habitats) were defined using k-means voxel-wise clustering. The reference standard was based on the pathology or Response Assessment in Neuro-Oncologycriteria for brain metastases (RANO-BM). The association between parameters of single-time or longitudinal tumor habitat and the time to recurrence and the site of recurrence were evaluated using the Cox proportional hazards regression analysis and Dice similarity coefficient, respectively. Results: The mean interval between the two MRI examinations was 99 days. The longitudinal analysis showed that an increase in the hypovascular cellular habitat (low ADC and low CBV) was associated with the risk of recurrence (hazard ratio [HR], 2.68; 95% confidence interval [CI], 1.46-4.91; P = 0.001). During the single-time analysis, a solid low-enhancing habitat (low T2 and low contrast-enhanced T1 signal) was associated with the risk of recurrence (HR, 1.54; 95% CI, 1.01-2.35; P = 0.045). A hypovascular cellular habitat was indicative of the future recurrence site (Dice similarity coefficient = 0.423). Conclusion: After SRS of brain metastases, an increased hypovascular cellular habitat observed using a longitudinal MRI analysis was associated with the risk of recurrence (i.e., treatment resistance) and was indicative of recurrence site. A tumor habitat analysis may help guide future treatments for patients with brain metastases.

Preliminary study on application of augmented reality visualization in robotic thyroid surgery

  • Lee, Dongheon;Kong, Hyoun-Joong;Kim, Donguk;Yi, Jin Wook;Chai, Young Jun;Lee, Kyu Eun;Kim, Hee Chan
    • Annals of Surgical Treatment and Research
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    • v.95 no.6
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    • pp.297-302
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    • 2018
  • Purpose: Increased robotic surgery is attended by increased reports of complications, largely due to limited operative view and lack of tactile sense. These kinds of obstacles, which seldom occur in open surgery, are challenging for beginner surgeons. To enhance robotic surgery safety, we created an augmented reality (AR) model of the organs around the thyroid glands, and tested the AR model applicability in robotic thyroidectomy. Methods: We created AR images of the thyroid gland, common carotid arteries, trachea, and esophagus using preoperative CT images of a thyroid carcinoma patient. For a preliminary test, we overlaid the AR images on a 3-dimensional printed model at five different angles and evaluated its accuracy using Dice similarity coefficient. We then overlaid the AR images on the real-time operative images during robotic thyroidectomy. Results: The Dice similarity coefficients ranged from 0.984 to 0.9908, and the mean of the five different angles was 0.987. During the entire process of robotic thyroidectomy, the AR images were successfully overlaid on the real-time operative images using manual registration. Conclusion: We successfully demonstrated the use of AR on the operative field during robotic thyroidectomy. Although there are currently limitations, the use of AR in robotic surgery will become more practical as the technology advances and may contribute to the enhancement of surgical safety.

Isozyme Analysis and Relationships Among Three Species in Malaysian Trichoderma Isolates

  • Siddiquee, Shafiquzzaman;Tan, Soon-Guan;Yusof, Umi-Kalsom
    • Journal of Microbiology and Biotechnology
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    • v.20 no.9
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    • pp.1266-1275
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    • 2010
  • Isozyme and protein electrophoresis data from mycelial extracts of 27 isolates of Trichoderma harzianum, 10 isolates of T. aureoviride, and 10 isolates of T. longibrachiatum from Southern Peninsular Malaysia were investigated. The eight enzyme and a single protein pattern systems were analyzed. Three isozyme and total protein patterns were shown to be useful for the detection of three Trichoderma species. The isozyme and protein data were analyzed using the Nei and Li Dice similarity coefficient for pairwise comparison between individual isolates, species isolate group, and for generating a distance matrix. The UPGMA cluster analysis showed a higher degree of relationship between T. harzianum and T. aureoviride than to T. longibrachiatum. These results suggested that the T. harzianum isolates had high levels of genetic variation compared with the other isolates of Trichoderma species.

Genetic Diversity in Rauvolfia tetraphylla L.f using RAPD Markers

  • Padmalatha, K;Prasad, MNV
    • Journal of Plant Biotechnology
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    • v.33 no.2
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    • pp.139-145
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    • 2006
  • The present study is the first report of molecular variations in different accessions of Rauvolfia tetraphylla L.f, a medicinally important plant collected from seven locations of Andhra Pradesh, India. Molecular analysis was carried out using RAPD markers. Out of the 40 primers screened from OPA and OPC Kts, a total of 205 scorable polymorphic markers out of 397 total markers were generated. Polymorphism of 51.6% was found with 3 unique markers. Levels of genetic diversity within accessions i.e., the genetic distance ranged from 0.816-0.932. Cluster analysis based on Dice coefficient showed two major groups indicating that mostly in cross-pollinated plants, high levels of differentiation among accessions exists independent of geographical distance. Hence the results of the present study can be seen as a starting point for future researches on the population and evolutionary genetics of this species. Understanding such variation would also facilitate their use in various conservational management practices, rootstock breeding and hybridisation programmes.

Comparative Analysis of Segmentation Methods in Psoriasis Area (건선 영역 분할기법 비교분석)

  • Yoo, Hyun-Jong;Lee, Ji-Won;Moon, Cho-I;Kim, Eun-Bin;Baek, Yoo-Sang;Jang, Sang-Hoon;Lee, OnSeok
    • Proceedings of the Korea Information Processing Society Conference
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    • 2019.10a
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    • pp.657-659
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    • 2019
  • 본 논문에서는 피부 이미지에서 건선 병변만을 가장 효과적으로 분할 할 수 있는 분할기법 선별을 목표로 한다. Interactive graph cuts (IGC)와 Level set method (LSM)를 사용하여 건선 영역을 분할한 후 Jaccard Index (JI)와 Dice Similarity Coefficient (DSC)을 사용하여 건선 영역에 효과적인 분할 방법을 제안한다.

Molecular Typing of Legionella pneumophila Isolated in Busan, Using PFGE (부산지역에서 분리한 레지오넬라균에 대한 PFGE를 이용한 molecular typing)

  • Park Eun-Hee;Kim Mi-Hee;Kim Joung-A;Han Nan-Sook;Lee Ju Hyeoun;Min Sang Gi;Park Yon Koung;Jin Seong Hyun;Jeong Gu Young;Bin Jae Hun
    • Journal of Life Science
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    • v.15 no.2 s.69
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    • pp.161-168
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    • 2005
  • In this study, we did the molecular typing of 39 environmental Legionella pneumophila serogroup 1 isolates collected from 2001-2003 in Busan using the pulsed-filed gel electrophoresis (PFGE). PFGE of SfiI fragments were divided into 10 pulsotypes $(A\~J)$, corresponding to $<65\%$ similarity and a subtype within each pulsotype was characterized by $>84\%$ similarity. The major cluster was pulsotype E $(46.2\%)$, which included 18 isolates and was divided into 4 subtypes $(E1\~E4)$. PFGE of NotI fragments were divided into 8 pulsotypes $(a\~h)$, corresponding to $<60\%$ similarity and a subtype within each pulsotype was characterized by $100\%$ similarity. The major cluster was pulsotype f $(38.5\%)$, which included 15 isolates. The ATCC type strain L. pneumophila serogroup 1 was identified as a different molecular pulsotype compare to the Busan isolates. It is possible that L. pneumophila serogroup 1 isolated in Busan with specific DNA pattern is comparable with those isolation in other cities in Korea.

Unsupervised Non-rigid Registration Network for 3D Brain MR images (3차원 뇌 자기공명 영상의 비지도 학습 기반 비강체 정합 네트워크)

  • Oh, Donggeon;Kim, Bohyoung;Lee, Jeongjin;Shin, Yeong-Gil
    • The Journal of Korean Institute of Next Generation Computing
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    • v.15 no.5
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    • pp.64-74
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    • 2019
  • Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

Substitutability of Noise Reduction Algorithm based Conventional Thresholding Technique to U-Net Model for Pancreas Segmentation (이자 분할을 위한 노이즈 제거 알고리즘 기반 기존 임계값 기법 대비 U-Net 모델의 대체 가능성)

  • Sewon Lim;Youngjin Lee
    • Journal of the Korean Society of Radiology
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    • v.17 no.5
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    • pp.663-670
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    • 2023
  • In this study, we aimed to perform a comparative evaluation using quantitative factors between a region-growing based segmentation with noise reduction algorithms and a U-Net based segmentation. Initially, we applied median filter, median modified Wiener filter, and fast non-local means algorithm to computed tomography (CT) images, followed by region-growing based segmentation. Additionally, we trained a U-Net based segmentation model to perform segmentation. Subsequently, to compare and evaluate the segmentation performance of cases with noise reduction algorithms and cases with U-Net, we measured root mean square error (RMSE) and peak signal to noise ratio (PSNR), universal quality image index (UQI), and dice similarity coefficient (DSC). The results showed that using U-Net for segmentation yielded the most improved performance. The values of RMSE, PSNR, UQI, and DSC were measured as 0.063, 72.11, 0.841, and 0.982 respectively, which indicated improvements of 1.97, 1.09, 5.30, and 1.99 times compared to noisy images. In conclusion, U-Net proved to be effective in enhancing segmentation performance compared to noise reduction algorithms in CT images.