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論文名稱(外文):A Support Vector Machine Based Skin Condition Inspection and Scoring System
外文關鍵詞:image processingfeature extractionsupport vector machineskin condition inspection
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為了取得臉部皮膚影像,我們設計了一個臉部影像擷取裝置,並以正常光與UV光取得臉部影像。之後我們以一序列之影像前處理步驟包含皮膚色彩分類、影像旋轉與校正、影像對位、型態學去雜訊與病徵區域分割來將臉部區域分割出來。用來判斷膚質之臉部病徵區域包含額頭、眼角、鼻頭、臉頰與下巴區域。這些病徵區域將由專家分別就我們所欲檢測與評分之病徵如青春痘、斑點、皺紋、黑眼圈與曬傷等症狀,擷取三種不同大小之區域,分別給定不同分數之專家意見。我們以病徵區域之色彩與紋理特徵值為訓練數據,透過主成分分析(Principal Component Analysis, PCA)降低資料維度,並以支持向量機(Support Vector Machine, SVM)進行訓練。
The skin condition affects people’s self-confidence, and it is also the symptom for many diseases. In this thesis, we handle the skin inspection problem. The skin data is extracted by a numbers of medical image processing techniques from the skin images in companion with the expertise from professionals. We have also developed a computerized detection and scoring algorithm to achieve the skin inspection goal.
We designed a facial image acquisition device to acquire the skin images with normal light and UV light. A series of image pre-processing steps including skin color classification, image rotation and correlation, image alignment, morphological noise removal and region of interested (ROI) detection, are performed. For skin inspection purpose, three different sizes of ROIs are extracted from the forehead region, the eyes region, the nose region, the cheek region and the chin region. These ROIs are assigned with a score by the professional experts on the basis of the skin conditions for acne, spot, wrinkle, black eye and sunburn symptoms. The features used in this study contain color and texture features. To reduce the high feature dimensions, the principal component analysis (PCA) is used. A support vector machine (SVM) training algorithm is developed to inspect the skin condition.
In this study, there are total 80 images are acquired with normal light and UV light. Each image is divided with 11 region of interested. Professionals assign each ROI with a score from 1 to 5 according to the severity of the skin diseases. For the skin image set, there are 50 images are used for training, and the other images are used for testing. The experimental result shows that the accuracy rates for the five symptoms are between 88% to 92%, and the average score differences are between 1.2 to 1.9. In this thesis, we have developed a cheap and effective skin condition inspection and scoring system to help people understand their own skin condition and provide a valuable reference for further treatment.
摘 要 IV
致 謝 VI
目  次 VII
表目錄 IX
圖目錄 X
第一章 緒論 1
1.1 背景與動機 1
1.2 研究目的 3
1.3 論文架構 3
第二章 文獻探討 4
2.1 膚色分割研究 4
2.2 特徵數據擷取 6
2.3 膚質病徵分類研究 7
第三章 系統架構 10
3.1 影像前處理 12
3.1.1 影像旋轉與校正 12
3.1.2 影像對位 13
3.1.3 形態學去雜訊 14
3.1.4 病徵之訓練區塊擷取 15
3.2 區塊特徵擷取 20
3.2.1 色彩空間特徵擷取 21
3.2.2 紋理特徵擷取 22
3.3 皮膚病徵分類 26
3.3.1 主成分分析(Principal Component Analysis, PCA) 26
3.3.2 支持向量機(Support Vector Machine, SVM) 30
3.3.3 膚質評分分析 33
第四章 實驗探討 35
4.1 系統開發與取像環境設置 35
4.2 膚質評分結果比較 37
4.3 最佳訓練區塊大小 39
4.4 最佳訓練特徵數據 41
第五章 結論 43
5.1 研究限制 43
5.1 未來展望 44
參考文獻 45
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