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.