Cell Detection with Gabor Filter-Based Features in Histopathologic Images

In this study, it was aimed to perform cell detection in histopathologic images which contains ground truth information. Firstly, in the patches which taken from the image, it is aimed to find out whether there is a certain frequency content in certain directions by using Gabor Filter. Features are extracted from the patches. Thus, training and test data sets were created. Then, using the training data sets, k-nearest neighbors, support vector machine, and random forest trained classification methods were used for classification. The success of classifications were observed with using test dataset and the results were presented.

[1]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[2]  Gokhan Bilgin,et al.  Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships , 2017, Medical & Biological Engineering & Computing.

[3]  Russell Greiner,et al.  Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach , 2009, AIME.

[4]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[5]  William E. Higgins,et al.  Efficient Gabor filter design for texture segmentation , 1996, Pattern Recognit..

[6]  B. S. Manjunath,et al.  A biosegmentation benchmark for evaluation of bioimage analysis methods , 2009, BMC Bioinformatics.

[7]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[8]  Fabio A. González,et al.  Histopathology Image Classification Using Bag of Features and Kernel Functions , 2009, AIME.

[9]  B. S. Manjunath,et al.  Evaluation and benchmark for biological image segmentation , 2008, 2008 15th IEEE International Conference on Image Processing.

[10]  Anil K. Jain,et al.  Fingerprint Image Enhancement: Algorithm and Performance Evaluation , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Marc Acheroy,et al.  Texture classification using Gabor filters , 2002, Pattern Recognit. Lett..

[12]  D. Larose.  k‐Nearest Neighbor Algorithm , 2005 .

[13]  Gökhan Bilgin.  Evaluation of spatial relations in the segmentation of histopathological images , 2013, 2013 21st Signal Processing and Communications Applications Conference (SIU).

[14]  Qinmu Peng,et al.  Cell Nucleus Segmentation in Color Histopathological Imagery Using Convolutional Networks , 2010, 2010 Chinese Conference on Pattern Recognition (CCPR).

[15]  Christian Igel,et al.  Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network , 2013, MICCAI.

[16]  Fabio A. González,et al.  A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection , 2013, MICCAI.

[17]  A. Madabhushi,et al.  Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.