24 February 2012 Digitized tissue microarray classification using sparse reconstruction
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In this paper, we propose a novel image classification method based on sparse reconstruction errors to discriminate cancerous breast tissue microarray (TMA) discs from benign ones. Sparse representation is employed to reconstruct the samples and separate the benign and cancer discs. The method consists of several steps including mask generation, dictionary learning, and data classification. Mask generation is performed using multiple scale texton histogram, integral histogram and AdaBoost. Two separate cancer and benign TMA dictionaries are learned using K-SVD. Sparse coefficients are calculated using orthogonal matching pursuit (OMP), and the reconstructive error of each testing sample is recorded. The testing image will be divided into many small patches. Each small patch will be assigned to the category which produced the smallest reconstruction error. The final classification of each testing sample is achieved by calculating the total reconstruction errors. Using standard RGB images, and tested on a dataset with 547 images, we achieved much better results than previous literature. The binary classification accuracy, sensitivity, and specificity are 88.0%, 90.6%, and 70.5%, respectively.
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Fuyong Xing, Fuyong Xing, Baiyang Liu, Baiyang Liu, Xin Qi, Xin Qi, David J. Foran, David J. Foran, Lin Yang, Lin Yang, } "Digitized tissue microarray classification using sparse reconstruction", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83144W (24 February 2012); doi: 10.1117/12.911900; https://doi.org/10.1117/12.911900

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