Classification of Hematoxylin and Eosin Images Using Local Binary Patterns and 1-D SIFT Algorithm

Abstract

Abstract:In this paper, Hematoxylin and Eosin (H&E) stained liver images are classified by using both Local Binary Patterns (LBP) and one dimensional SIFT (1-D SIFT) algorithm. In order to obtain more meaningful features from the LBP histogram, a new feature vector extraction process is implemented for 1-D SIFT algorithm. LBP histograms are extracted with different approaches and concatenated with color histograms of the images. It is experimentally shown that,with the proposed approach, it possible to classify the H&E stained liver images with the accuracy of 88%.

A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes

“…Therefore, the information (descriptors) extracted from each patch becomes the key to a successful tissue classification. Generic descriptors, such as HOG [17], LBP [18], SIFT [19] or Gabor filters [20] are frequently used for prostate cancer detection.…”

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

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