Microscopic images classification for cancer diagnosis

Computer aided diagnosis of cancer is a field of substantial worth in current scenario since approximately 38% population of the world is suffering from the disease. The detection of cancer is based on the observation of deformation in nuclei structure using histopathology slides/images. The proposed technique utilizes nuclei localization prior to classification of histopathology images as benign and malignant. The features used for classification are an ensemble of 150 bag of visual word features, extracted from preprocessed image and 20 handcrafted features, extracted from the internal parts of nuclei using localized histopathology images. The simulation results confirm the superiority of proposed localization based cancer classification method as compared to existing methods of the domain. It has reported average classification accuracy of 95.03% on BreakHis dataset.

[1]  Luiz Eduardo Soares de Oliveira,et al.  A Dataset for Breast Cancer Histopathological Image Classification , 2016, IEEE Transactions on Biomedical Engineering.

[2]  S. S. Vinod Chandra,et al.  Long-Term Forecasting the Survival in Liver Transplantation Using Multilayer Perceptron Networks , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  Metin Nafi Gürcan,et al.  Computerized classification of intraductal breast lesions using histopathological images , 2011, IEEE Transactions on Biomedical Engineering.

[4]  Wei Yang,et al.  Neighborhood Component Feature Selection for High-Dimensional Data , 2012, J. Comput..

[5]  Chanho Jung,et al.  Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization , 2010, IEEE Transactions on Biomedical Engineering.

[6]  Vishal Monga,et al.  Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning , 2015, IEEE Transactions on Medical Imaging.

[7]  Linda G. Shapiro,et al.  Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study , 2014, 2014 22nd International Conference on Pattern Recognition.

[8]  Fa Zhang,et al.  Breast cancer histopathological image classification using a hybrid deep neural network. , 2020, Methods.

[9]  Gustavo K. Rohde,et al.  An Optimal Transportation Approach for Nuclear Structure-Based Pathology , 2011, IEEE Transactions on Medical Imaging.

[10]  Anindya Sarkar,et al.  Prostate Cancer Grading: Use of Graph Cut and Spatial Arrangement of Nuclei , 2014, IEEE Transactions on Medical Imaging.

[11]  Aaron Klein,et al.  Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets , 2016, AISTATS.

[12]  Konstantinos N. Plataniotis,et al.  Circular Mixture Modeling of Color Distribution for Blind Stain Separation in Pathology Images , 2017, IEEE Journal of Biomedical and Health Informatics.

[13]  Ghassan Hamarneh,et al.  Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells , 2017, IEEE Journal of Biomedical and Health Informatics.

[14]  Ambuj K. Singh,et al.  Bisque: a platform for bioimage analysis and management , 2009, Bioinform..

[15]  H. Irshad,et al.  Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential , 2014, IEEE Reviews in Biomedical Engineering.

[16]  George Azzopardi,et al.  Contour Detection by CORF Operator , 2012, ICANN.

[17]  Yashwant Kurmi,et al.  Tumor Malignancy Detection Using Histopathology Imaging. , 2019, Journal of medical imaging and radiation sciences.

[18]  Jacob D. Jaffe,et al.  Plasticity in patterns of histone modifications and chromosomal proteins in Drosophila heterochromatin. , 2011, Genome research.

[19]  Thomas Walter,et al.  Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map , 2019, IEEE Transactions on Medical Imaging.

[20]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[21]  Bo Wang,et al.  Weakly supervised mitosis detection in breast histopathology images using concentric loss , 2019, Medical Image Anal..

[22]  Yongming Li,et al.  Automatic cell nuclei segmentation and classification of breast cancer histopathology images , 2016, Signal Process..

[23]  Zhenzhou Wang,et al.  A semi-automatic method for robust and efficient identification of neighboring muscle cells , 2016, Pattern Recognit..

[24]  Allan Hanbury,et al.  An Efficient Algorithm for Calculating the Exact Hausdorff Distance , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  P. Jaccard.  THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[26]  Stephen J. McKenna,et al.  Structure Prediction for Gland Segmentation With Hand-Crafted and Deep Convolutional Features , 2018, IEEE Transactions on Medical Imaging.

[27]  Yashwant Kurmi,et al.  Multifeature-based medical image segmentation , 2018, IET Image Process..

[28]  Naima Iltaf,et al.  Image Contrast Enhancement Using Weighted Transformation Function , 2016, IEEE Sensors Journal.

[29]  Gyan Bhanot,et al.  Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology , 2010, IEEE Transactions on Biomedical Engineering.

[30]  Vijayshri Chaurasia,et al.  Statistical feature extraction based technique for fast fractal image compression , 2016, J. Vis. Commun. Image Represent..

[31]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..