TY - GEN
T1 - Studying the effect of digital stain separation of histopathology images on image search performance
AU - Cheeseman, Alison K.
AU - Tizhoosh, Hamid R.
AU - Vrscay, Edward R.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Due to recent advances in technology, digitized histopathology images are now widely available for both clinical and research purposes. Accordingly, research into computerized image analysis algorithms for digital histopathology images has been progressing rapidly. In this work, we focus on image retrieval for digital histopathology images. Image retrieval algorithms can be used to find similar images and can assist pathologists in making quick and accurate diagnoses. Histopathology images are typically stained with dyes to highlight features of the tissue, and as such, an image analysis algorithm for histopathology should be able to process colour images and determine relevant information from the stain colours present. In this study, we are interested in the effect that stain separation into their individual stain components has on image search performance. To this end, we implement a basic k-nearest neighbours (kNN) search algorithm on histopathology images from two publicly available data sets (IDC and BreakHis) which are: a) converted to greyscale, b) digitally stain-separated and c) the original RGB colour images. The results of this study show that using H&E separated images yields search accuracies within one or two percent of those obtained with original RGB images, and that superior performance is observed using the H&E images in most scenarios we tested.
AB - Due to recent advances in technology, digitized histopathology images are now widely available for both clinical and research purposes. Accordingly, research into computerized image analysis algorithms for digital histopathology images has been progressing rapidly. In this work, we focus on image retrieval for digital histopathology images. Image retrieval algorithms can be used to find similar images and can assist pathologists in making quick and accurate diagnoses. Histopathology images are typically stained with dyes to highlight features of the tissue, and as such, an image analysis algorithm for histopathology should be able to process colour images and determine relevant information from the stain colours present. In this study, we are interested in the effect that stain separation into their individual stain components has on image search performance. To this end, we implement a basic k-nearest neighbours (kNN) search algorithm on histopathology images from two publicly available data sets (IDC and BreakHis) which are: a) converted to greyscale, b) digitally stain-separated and c) the original RGB colour images. The results of this study show that using H&E separated images yields search accuracies within one or two percent of those obtained with original RGB images, and that superior performance is observed using the H&E images in most scenarios we tested.
KW - Digital histopathology
KW - Digital image retrieval and classification
KW - Digital stain separation
KW - Encoded Local Projections (ELP)
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U2 - 10.1007/978-3-030-50516-5_23
DO - 10.1007/978-3-030-50516-5_23
M3 - Conference contribution
AN - SCOPUS:85087284516
SN - 9783030505158
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 262
EP - 273
BT - Image Analysis and Recognition - 17th International Conference, ICIAR 2020, Proceedings
A2 - Campilho, Aurélio
A2 - Karray, Fakhri
A2 - Wang, Zhou
PB - Springer
T2 - 17th International Conference on Image Analysis and Recognition, ICIAR 2020
Y2 - 24 June 2020 through 26 June 2020
ER -