Forming Local Intersections of Projections for Classifying and Searching Histopathology Images

In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images. The descriptor is based on the Radon transform wherein we apply parallel projections in small local neighborhoods of gray-level images. Using equidistant projection directions in each window, we extract unique and invariant characteristics of the neighborhood by taking the intersection of adjacent projections. Thereafter, we construct a histogram for each image, which we call the FLIP histogram. Various resolutions provide different FLIP histograms which are then concatenated to form the mFLIP descriptor. Our experiments included training common networks from scratch and fine-tuning pre-trained networks to benchmark our proposed descriptor. Experiments are conducted on the publicly available dataset KIMIA Path24 and KIMIA Path960. For both of these datasets, FLIP and mFLIP descriptors show promising results in all experiments.Using KIMIA Path24 data, FLIP outperformed non-fine-tuned Inception-v3 and fine-tuned VGG16 and mFLIP outperformed fine-tuned Inception-v3 in feature extracting.

[1]  L. Pantanowitz Digital images and the future of digital pathology , 2010, Journal of pathology informatics.

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  M. R. Vant,et al.  Application Of Radon Transform Techniques To Wake Detection In Seasat-A SAR Images , 1990 .

[4]  Hamid R. Tizhoosh,et al.  Barcodes for medical image retrieval using autoencoded Radon transform , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[5]  Hai Su,et al.  Cell Encoding for Histopathology Image Classification , 2017, MICCAI.

[6]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[7]  Hamid R. Tizhoosh,et al.  Local radon descriptors for image search , 2017, 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA).

[8]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[9]  Cesare Furlanello,et al.  Evaluating reproducibility of AI algorithms in digital pathology with DAPPER , 2018, bioRxiv.

[10]  Yan Xu,et al.  Deep learning of feature representation with multiple instance learning for medical image analysis , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Hamid R. Tizhoosh,et al.  Representing Medical Images With Encoded Local Projections , 2018, IEEE Transactions on Biomedical Engineering.

[12]  Matti Pietikäinen,et al.  Local Binary Patterns for Still Images , 2011 .

[13]  W. F. Lever,et al.  Histopathology of the Skin , 1962 .

[14]  K Kayser,et al.  Pattern recognition in histopathology by orders of textures. , 1984, Medical informatics = Medecine et informatique.

[15]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Hamid R. Tizhoosh,et al.  Convolutional neural networks for histopathology image classification: Training vs. Using pre-trained networks , 2017, 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA).

[17]  Hai Su,et al.  Supervised graph hashing for histopathology image retrieval and classification , 2017, Medical Image Anal..

[18]  Navid Farahani,et al.  whole slide imaging in pathology: advantages, limitations, and emerging perspectives , 2015 .

[19]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[20]  Hamid R. Tizhoosh,et al.  A comparative study of CNN, BoVW and LBP for classification of histopathological images , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[21]  Hamid R. Tizhoosh,et al.  MinMax Radon Barcodes for Medical Image Retrieval , 2016, ISVC.

[22]  Jorge L. C. Sanz,et al.  Radon and projection transform-based computer vision , 1988 .

[23]  Chi-Ho Chan Multi-scale local Binary Pattern Histogram for Face Recognition , 2007, ICB.

[24]  Jorge L. C. Sanz,et al.  Radon and Projection Transform-Based Computer Vision: Algorithms, A Pipeline Architecture, and Industrial Applications , 1988 .

[25]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[26]  Hamid R. Tizhoosh,et al.  Barcode annotations for medical image retrieval: A preliminary investigation , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[27]  Shahryar Rahnamayan,et al.  Classification and Retrieval of Digital Pathology Scans: A New Dataset , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).