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์์ ๊ธฐ๋ฐ ๊ธฐ๊ณํ์ต์ ์ด์ฉํ ๋ผ์ง ํ๋ณ๋ณ์ ์ง๋จ
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ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์์๊ณผ๋ํ ์์ํ๊ณผ, 2018. 2. ๊น์ฉ๋ฐฑ.์๋ ์ ์ง๊ตญ์์ ๋์ฒด๊ฒ์ฌ๋ ์ํ ์์ ๋ฐ ๋ผ์ง ์ง๋ณ ๋ชจ๋ํฐ๋ง ๋ฐ ๋ฐฉ์ญ ๊ณํ ์๋ฆฝ์ ์ฌ์ฉ๋๊ณ ์๋ค. ํ์ฌ ์์ ์ง๋จ ์์ญ์์ ์ ์ฉ๋๊ณ ์๋ ๊ธฐ๊ณํ์ต ๊ธฐ๋ฒ ๊ธฐ๋ฐ์ ์ปดํจํฐ ์ง๋จ์ ๋์ฒด๊ฒ์ฌ์ ์ ์ฉ์ํจ๋ค๋ฉด ํ, ๊ฐ, ์ฅ ๋ฑ์ ์ฅ๊ธฐ ๊ฒ์ฌ๋ฅผ ๋ณด๋ค ๊ฐ์ ์ํฌ ์ ์์ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ์์ ๊ธฐ๋ฐ ์ง๋จ ์์คํ
๊ตฌ์ถ์ ์ํ ์ ํ์ฐ๊ตฌ๋ก์จ ๋ผ์ง ํ์ ์ฌ์ง์ ํตํด ํ๊ฐํ ํ๋ณ๋ณ์ง์์ ๊ธฐ๊ด์ง์ฑ ํ๋ ด๊ณผ์ ์๊ด์ฑ์ด ์กฐ์ฌ๋์๋ค. ํ ์กฐ์ง๊ณผ ํ๋ณ๋ณ ์ด๋ฏธ์ง๋ ๋์ถ์ฅ์์ ์์ง๋์๋ค. ํ๋ณ๋ณ๋ฅผ ํตํด ํ๋ผ๋ณ์ง์๊ฐ ํ๊ฐ๋์์ผ๋ฉฐ ์กฐ์ง๋ณ๋ฆฌํ์ ๊ฒ์ฌ๋ฅผ ํตํด ํ๋ณ๋ณ์ ๊ธฐ๊ด์ง์ฑํ๋ ด๊ณผ ๊ฐ์ง์ฑ ํ๋ ด์ผ๋ก ๊ตฌ๋ถ๋์๋ค. ํ๋ณ๋ณ ์ง์๋ 90% ์ ๋ขฐ๊ตฌ๊ฐ์์ ๊ธฐ๊ด์ง์ฑ ํ๋ ด์ ๋ํด 100%์ ๋ฏผ๊ฐ๋์ 77.3%์ ํน์ด๋๋ฅผ ์ง๋๋ ๊ฒ์ผ๋ก ํ์ธ๋์๋ค. ๊ทธ๋ฌ๋ ๊ธฐ๊ด์ง์ฑํ๋ ด์ ์ง๋จ์ ๋ํด์๋ ํน์ด๋๊ฐ ๋ฎ์๋ค. ์์ ์ ์กฐ์ํน์ฑ ๊ณก์ ์๋ ๋ฉด์ ์ 0.896์ผ๋ก, ํ๋ณ๋ณ ์ง์๋ ๊ธฐ๊ด์ง์ฑ ํ๋ ด์ ๋ํ ๊ฐ๋ณ๋ ฅ์ด ์ฐ์ํ ๊ฒ์ผ๋ก ํ๋จ๋์๋ค. ์ง๋ณ ํ์ ์ฌ์ง์ ํตํ ์๊ฐ์ ๋ณด๊ฐ ์คํฌ๋ฆฌ๋ ํ
์คํธ์ ์ ์ฉํ๊ฒ ์์ฉํ์์ผ๋ฏ๋ก, ํ ์ฌ์ง์ ํ์ต ๋ฐ์ดํฐ์งํฉ์ผ๋ก ๊ตฌ์ฑํ์ฌ ์์ ๊ธฐ๋ฐ ๊ธฐ๊ณํ์ต์ ํตํ ๋ผ์ง ํ๋ณ๋ณ ์ง๋จ ๋ชจ๋ธ์ ์๋ฆฝํ์๋ค. ํ๋ณ๋ณ ์ด๋ฏธ์ง์ ํ ์กฐ์ง์ ๋์ถ์ฅ์ผ๋ก๋ถํฐ ์์ง๋์์ผ๋ฉฐ, ํ ๋ณ๋ณ์ ์กฐ์ง๋ณ๋ฆฌํ์ ์ผ๋ก ๊ธฐ๊ด์ง์ฑ ํ๋ ด, ๊ฐ์ง์ฑ ํ๋ ด, ๊ทธ๋ฆฌ๊ณ ํ๋ง์ฑ ์งํ๋ค๋ก ๋ถ๋ฅ๋์๋ค. ์ง๋จ ๋ชจ๋ธ์ ๊ตฌ์ฑํจ์ ์์ด ์ฌ์ง์ ์ฃผ์ ํน์ง์ ์ ์ถ์ถํ๋ ๊ธฐ๋ฒ์ผ๋ก scale-invariant feature transform์ด ์ ์ฉ๋์์ผ๋ฉฐ, ์ถ์ถ๋ ํน์ง์ ์ ๋ถ๋ฅํ๊ธฐ ์ํ ๊ธฐ๊ณํ์ต ๋ถ๋ฅ ๊ธฐ๋ฒ์ผ๋ก k-nearest neighbor์ด ์ ์ฉ๋์๋ค. ์ด๋ฏธ์ง ๊ธฐ๋ฐ์ ์ง๋จ ๋ชจ๋ธ์ ๊ธฐ๊ด์ง์ฑ ํ๋ ด์ ๋ํด์ 96.7%์ ๋์ ๋ฏผ๊ฐ๋์ 72.3%์ ํน์ด๋, ๊ทธ๋ฆฌ๊ณ 82.0%์ ์ ํ๋๋ฅผ ๋ณด์์ผ๋ฉฐ, ๊ฐ์ง์ฑ ํ๋ ด์ ๋ํด์๋ 9.4%์ ๋์ ํน์ด๋์ 87.4%์ ์ ํ๋, ๊ทธ๋ฆฌ๊ณ 75.8%์ ๋ฏผ๊ฐ๋๋ฅผ ๋ณด์๋ค. ๊ทธ๋ฌ๋ ํ๋ง์ผ ๋ฐ ํ๋ง ํ๋ ด์ ์ง๋จ์ ์์ด์๋ ๋น๊ต์ ๋ฎ์ ์ฑ๋ฅ์ด ํ์ธ๋์๋ค. ๋ณธ ์ฐ๊ตฌ๋ ๋์ถ์ฅ์์์ ๋์ฒด๊ฒ์ฌ์ ์ปดํจํฐ ๋ณด์กฐ์ง๋จ์ ์ ์ฉ ๊ฐ๋ฅ์ฑ์ ํ์งํ๋, ์์ ๊ธฐ๋ฐ ๊ธฐ๊ณํ์ต์ ์ด์ฉํ ์ฅ๊ธฐ๊ฒ์ฌ์ ๋ํ ์๋ก์ด ์ ๊ทผ๋ฒ์ ์ ์ํ์๋ค. ๋ณธ ์ฐ๊ตฌ์์ ์ป์ด์ง ์ ๋ณด๋ค์ ์์ ์ง๋จ ์์ญ์์ ์์์ ๊ธฐ๋ฐ์ผ๋ก ํ ์ปดํจํฐ ๋์ฒด๊ฒ์ฌ์ ์ด์์ด ๋ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.Slaughter check system has been applied to improve food hygiene and swine health schemes in the countries with advanced swine industry. Lung inspection is the most critical part of the slaughter check system. Recent advance in computer vision technology has led to the development of computer-aided diagnosis (CAD). As a pilot study prior to organ inspection using CAD, the correlation between gross lung scoring and pathologic diagnosis was investigated. Lung tissues and their gross images were collected from slaughterhouses. The images were subjected to gross lung lesion scoring. Histopathologic examination was conducted to classify the lung lesions into bronchopneumonia and interstitial pneumonia. The gross lung lesion scoring revealed 100% of sensitivity and 77.3% of specificity for bronchopneumonia based on the 90% confidence interval. However, the specificity was low for the diagnosis of interstitial pneumonia. The area under receiver operation characteristic curve was 0.896, indicating a good discriminative performance of gross lung scoring for bronchopneumonia. Taken together, the data indicated that visual information of the photograph was useful to screen lung lesions. Further study was performed to establish a CAD model for swine pulmonary diseases. Lung tissues and the gross images were collected from the slaughterhouses and the lung lesions were histopathologically classified as bronchopneumonia, interstitial pneumonia, and pleural diseases. The scale-invariant feature transform algorithm was adopted to extract significant feature from the images. As a machine learning classification, k-nearest neighbor algorithm was applied to classify the extracted feature. For bronchopneumonia, the CAD model demonstrated the sensitivity of 96.7%, the specificity of 72.3 %, and accuracy of 82.0%. For interstitial pneumonia, the sensitivity was 75.8%, but the specificity and accuracy were high as 94.4% and 87.4%, respectively. However, it showed low performance for the diagnostic classification of pleural diseases. The present study provided a new approach of organ inspection through image-based machine learning, giving insight into application of CAD to slaughter check system. The data presented in this study could be a cornerstone for the development of computational image-based organ inspection system in veterinary diagnostics.LITERATURE REVIEW 1
Introduction 1
SRDCs in swine industry 2
CAD application into veterinary field 3
Computer vision and machine learning technique applied to CAD 4
Purpose of the present study 6
CHAPTER 1. EVALUATION OF CORRELATION BETWEEN GROSS LUNG SCORE AND MICROSCOPIC DIAGNOSIS FOR SWINE PNEUMONIA IN SLAUTHER HOUSES 7
Abstract 8
Introduction 9
Materials and methods 12
Collection of tissue samples and gross images from swine lung 12
Histopathological classification 12
Evaluation of visual lung score 13
Statistical analysis 13
Results 14
Visual inspection for primary classification of swine pneumonia 14
Histopathological examination for confirmative diagnosis 14
Analysis of visual lung score between histopathologically classified bronchopneumonia and
interstitial pneumonia 15
Diagnostic assessment of visual lung score method for pneumonia classification 16
Discussion 24
CHAPTER 2. CLASSIFICATION OF SWINE LUNG LESIONS BY A COMPUTER-AIDED DIAGNOSTIC MODEL CREATED
USING IMAGE-BASED MACHINE LEARNING ALGORITHM 29
Abstract 30
Results 39
Discussion 53
Conclusion 59
References 60
๊ตญ๋ฌธ์ด๋ก 71Maste
A Study on Multi-layered Expressions of Identity Marks
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ผ๋ฌธ(์์ฌ) --์์ธ๋ํ๊ต ๋ํ์ :๋์์ธํ๋ถ,2007.Maste
๊ฒฉ์๊ตฌ์กฐ ๊ด์์์ ํด์ :I. Distributed Feedback ๋ ์ด์ ์์ Spatial Hole Burning ํจ๊ณผ์ ์ต์ํ :II. ํผํฉ๋ค์คํ ํ๋ก๊ทธ๋จ ๋ฉ๋ชจ๋ฆฌ์์์ Cross-talk ํด์
Thesis (doctoral)--์์ธ๋ํ๊ต ๋ํ์ :์ ๊ธฐ๊ณตํ๋ถ,1999.Docto