  In recent years, histopathology images have been increasingly used as a
diagnostic tool in the medical field. The process of accurately diagnosing a
biopsy sample requires significant expertise in the field, and as such can be
time-consuming and is prone to uncertainty and error. With the advent of
digital pathology, using image recognition systems to highlight problem areas
or locate similar images can aid pathologists in making quick and accurate
diagnoses. In this paper, we specifically consider the encoded local
projections (ELP) algorithm, which has previously shown some success as a tool
for classification and recognition of histopathology images. We build on the
success of the ELP algorithm as a means for image classification and
recognition by proposing a modified algorithm which captures the local
frequency information of the image. The proposed algorithm estimates local
frequencies by quantifying the changes in multiple projections in local windows
of greyscale images. By doing so we remove the need to store the full
projections, thus significantly reducing the histogram size, and decreasing
computation time for image retrieval and classification tasks. Furthermore, we
investigate the effectiveness of applying our method to histopathology images
which have been digitally separated into their hematoxylin and eosin stain
components. The proposed algorithm is tested on the publicly available invasive
ductal carcinoma (IDC) data set. The histograms are used to train an SVM to
classify the data. The experiments showed that the proposed method outperforms
the original ELP algorithm in image retrieval tasks. On classification tasks,
the results are found to be comparable to state-of-the-art deep learning
methods and better than many handcrafted features from the literature.
