Color wavelet covariance(CWC) Texture feature 2013-06-04 Jo Yeong-Jun Karkanis, Stavros A., et al. "Computer-aided tumor detection in endoscopic video using color wavelet features."  Information Technology in Biomedicine, IEEE Transactions on 7.3 (2003): 141-152.

Content Introduction Haralick texture features Color wavelet covariance(CWC) texture feature DWT transformation Statistical measures (from haralick) Covariance between color channels Experimental Results

Introduction

Introduction What is texture? Texture의 사전적 정의 Image texture Computer-aided tumor detection in endoscopic video using color wavelet features Introduction What is texture? Texture의 사전적 정의 질감(質感) : 재질의 차이에서 받는 느낌. Image texture Pixel intensities의 변화에 따른 반복적 패턴. 이미지의 색 배열, intensities 등의 정보를 나타냄. 공개 Database Brodatz Vistex Textures Textures Artificial texture Natural texture

Introduction Color wavelet covariance feature(CWC) Computer-aided tumor detection in endoscopic video using color wavelet features Introduction Color wavelet covariance feature(CWC) Color wavelet covariance(CWC) feature는 텍스처 분석 feature. 2003년 CWC가 대장 용종 검출에 사용된 후, 용종 검출 뿐만 아니라 의료 영상 분석에 많이 사용됨. 1973년에 제안된 haralick texture feature[1] 중 distinctiveness가 있는 몇 개의 measure를 이용. Discrete wavelet transform(DWT)를 통해 texture를 잘 표현하는 주파수 대역을 이용 [2]. 최종적으로 Color space(RGB)간의 관계를 feature로 정의 함 [3]. [1] Haralick, Robert M., Karthikeyan Shanmugam, and Its' Hak Dinstein. "Textural features for image classification." Systems, Man and Cybernetics, IEEE Transactions on 6 (1973): 610-621. [2] Julesz, Bela. "Texton gradients: The texton theory revisited." Biological Cybernetics 54.4-5 (1986): 245-251. [3] Van de Wouwer, Gert, et al. "Wavelet correlation signatures for color texture characterization." Pattern recognition 32.3 (1999): 443-451.

Haralick Texture features

Haralick texture features Computer-aided tumor detection in endoscopic video using color wavelet features Haralick texture features Assumption 이미지의 각 픽셀은 주변 픽셀과 공간적인 관계가 있음. Gray level co-occurrence matrix(GLCM) 픽셀간의 공간적 관계를 나타내는 행렬 0˚ 45˚ 90˚ 135˚ 0 1 2 3 1 2 3 1 2 3 1 2 1 3 1 2 1 1 0.16 0.08 0.25 4 gray-level image GLCM Normalized GLCM

Haralick texture features Computer-aided tumor detection in endoscopic video using color wavelet features Haralick texture features Extract features from GLCM 제안된 14가지의 statistical features를 GLCM에서 계산. 각각의 feature는 각기 다른 통계적 성질을 분석하는 measure. 0.018 3.048 0.807 1.152 0.632 1.231 0.125 0.002 5.123 0.721 2.112 0.521 0.892 12.52 0.16 0.08 0.25 Feature Extraction GLCM Haralick feature (14 dimension)

Haralick texture features Computer-aided tumor detection in endoscopic video using color wavelet features Haralick texture features Ex : 두가지 이미지에서 뽑힌 haralick features Grassland Ocean Angle 45 90 135 Avg ASM Contrast Correlation .0128 3.048 .8075 .0080 4.011 .6366 .0077 4.014 .5987 .0064 4.709 .4610 .0087 3.945 .6259 ASM Contrast Correlation .1016 2.153 .7254 .0771 3.057 .4768 .0762 3.113 .4646 .0741 3.129 .4650 .0822 2.863 .5327

Color wavelet covariance Texture features

Color wavelet covariance(CWC) texture feature Computer-aided tumor detection in endoscopic video using color wavelet features Color wavelet covariance(CWC) texture feature Haralick features의 14가지 feature중 가장 분별력 있는 4가지 feature 조합을 이용. Angular second moment Correlation Inverse difference moment Entropy 14가지 중 서로 correlation이 작은 것들을 선택함. Correlation이 크면 dependent하기 때문에 쓸데 없는 정보일 수 있음. 14 haralick statistics measures

Color wavelet covariance(CWC) texture feature Computer-aided tumor detection in endoscopic video using color wavelet features Color wavelet covariance(CWC) texture feature Discrete wavelet transform(DWT) 이용 H(z) ↓ L(z) HH HL LH LL Column 방향 H(z) L(z) ↓ Row 방향 이미지 압축 기법인 JPEG2000에서 사용. 각각의 변환 이미지들은 각 대역폭에서의 밝기 변화를 묘사. 이미지의 패턴분석에 사용. LL LH HL HH 3 level DWT

Color wavelet covariance(CWC) texture feature Computer-aided tumor detection in endoscopic video using color wavelet features Color wavelet covariance(CWC) texture feature 3 level DWT 에서 texture를 가장 잘 표현하는 level을 이용. Julesz, Bela.[2] 에 따르면, DWT의 Second-order 정보가 가장 Texture를 잘 나타낸다고 알려짐. R(i=1) g(i=2) B(i=3) (3- channel) 𝐷 𝑙 𝑖 , 𝑖=1,2,3, 𝑙=4,5,6 3 level DWT [2] Julesz, Bela. "Texton gradients: The texton theory revisited." Biological Cybernetics 54.4-5 (1986): 245-251.

Color wavelet covariance(CWC) texture feature Computer-aided tumor detection in endoscopic video using color wavelet features Color wavelet covariance(CWC) texture feature R(i=1) g(i=2) B(i=3) (3- channel) 𝐷 𝑙 𝑖 , 𝑖=1,2,3, 𝑙=4,5,6 𝐶 𝑎 𝐷 𝑙 𝑖 , 𝑖=1,2,3, 𝑙=4,5,6 , 𝛼=0°, 45°, 90°, 135° 𝛼= Co-occurrence matrix 1. Angular second moment 2. Correlation 3. Inverse difference moment 4. Entropy 𝐹 𝑚 𝐶 𝑎 𝐷 𝑙 𝑖 , 𝑖=1,2,3, 𝑙=4,5,6 , 𝛼=0°, 45°, 90°, 135°, 𝑚=1,2,3,4 𝑚= 4 Haralick features

Color wavelet covariance(CWC) texture feature Computer-aided tumor detection in endoscopic video using color wavelet features Color wavelet covariance(CWC) texture feature 마지막으로 color space들 간의 covariance를 고려. Van de Wouwer, Gert, et al.[3] 에 따르면, 이미지의 각 color space들은 서로 밀접한 관계를 가진다. 이러한 경향성을 이용하면 효과적으로 Texture 를 정의 할 수 있다. 𝑖=1,2,3, 𝑙=4,5,6 , 𝛼=0°, 45°, 90°, 135°, 𝑚=1,2,3,4 𝐹 𝑚 𝐶 𝑎 𝐷 𝑙 𝑖 , 𝐶𝑊𝐶 𝑚 𝑙 (𝑖,𝑗)=𝐶𝑜𝑣 𝐹 𝑚 𝐶 𝑎 𝐷 𝑙 𝑖 , 𝐹 𝑚 𝐶 𝑎 𝐷 𝑙 𝑗 [3] Van de Wouwer, Gert, et al. "Wavelet correlation signatures for color texture characterization." Pattern recognition 32.3 (1999): 443-451.

Color wavelet covariance(CWC) texture feature Computer-aided tumor detection in endoscopic video using color wavelet features Color wavelet covariance(CWC) texture feature 과정 정리 𝐷 4 𝐷 5 𝐷 6 Co-occurrence matrices 𝐶 𝑎 𝐷 𝑙 𝑖 , 𝑖=1,2,3, 𝑙=4,5,6 , 𝛼=0°, 45°, 90°, 135° R(i=1) g(i=2) B(i=3) level DWT (3- channel) 𝐷 𝑙 𝑖 , 𝑖=1,2,3, 𝑙=4,5,6 Input image 𝐶𝑊𝐶 𝑚 𝑙 𝑖,𝑗 = 𝐶𝑜𝑣 𝐹 𝑚 𝐶 𝑎 𝐷 𝑙 𝑖 , 𝐹 𝑚 𝐶 𝑎 𝐷 𝑙 𝑗 각 채널에서 추출된 Feature간 Covariance 계산 Statistical feature extraction 𝐹 𝑚 𝐶 𝑎 𝐷 𝑙 𝑖 , 𝑖=1,2,3, 𝑙=4,5,6 , 𝛼=0°, 45°, 90°, 135°, 𝑚=1,2,3,4 1. ASM 2. Correlation 3. IDM 4. Entropy

Computer-aided tumor detection in endoscopic video using color wavelet features Experimental results

Experimental results Specificity = True negative/전체 Negative Computer-aided tumor detection in endoscopic video using color wavelet features Experimental results Specificity = True negative/전체 Negative Sensitivity = True positive /전체 Positive

Computer-aided tumor detection in endoscopic video using color wavelet features Experimental results

Experimental results [1] Iakovidis, D. K., et al. "A comparative study of texture features for the discrimination of gastric polyps in endoscopic video." Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on. IEEE, 2005. [2] Alexandre, Luís A., Nuno Nobre, and João Casteleiro. "Color and position versus texture features for endoscopic polyp detection." BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on. Vol. 2. IEEE, 2008.

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