Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma

Abstract. The purpose of this study is to investigate the utility of obtaining “core samples” of regions in CT volume scans for extraction of radiomic features. We asked four readers to outline tumors in three representative slices from each phase of multiphasic liver CT images taken from 29 patients (1128 segmentations) with hepatocellular carcinoma. Core samples were obtained by automatically tracing the maximal circle inscribed in the outlines. Image features describing the intensity, texture, shape, and margin were used to describe the segmented lesion. We calculated the intraclass correlation between the features extracted from the readers’ segmentations and their core samples to characterize robustness to segmentation between readers, and between human-based segmentation and core sampling. We conclude that despite the high interreader variability in manually delineating the tumor (average overlap of 43% across all readers), certain features such as intensity and texture features are robust to segmentation. More importantly, this same subset of features can be obtained from the core samples, providing as much information as detailed segmentation while being simpler and faster to obtain.

[1]  Akinobu Shimizu,et al.  3D extension of Haralick texture features for medical image analysis , 2007 .

[2]  Lucia Dettori,et al.  A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography , 2007, Comput. Biol. Medicine.

[3]  Xing Zhang,et al.  Interactive liver tumor segmentation from ct scans using support vector classification with watershed , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  M. Giger,et al.  Automatic segmentation of breast lesions on ultrasound. , 2001, Medical physics.

[5]  K Doi,et al.  Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. , 1987, Medical physics.

[6]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[7]  Hamid Soltanian-Zadeh,et al.  Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms , 2004, Pattern Recognit..

[8]  D. Rubin,et al.  Automated tracking of quantitative assessments of tumor burden in clinical trials. , 2014, Translational oncology.

[9]  Jacob D. Furst,et al.  Wavelet-based texture classification of tissues in computed tomography , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[10]  Marios S. Pattichis,et al.  Fast localization of optic disc and fovea in retinal images for eye disease screening , 2011, Medical Imaging.

[11]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[12]  Yi Liu,et al.  On the Feasibility of Predicting Radiological Observations from Computational Imaging Features of Liver Lesions in CT Scans , 2011, 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology.

[13]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[14]  Yanxi Liu,et al.  Cervical Cancer Detection Using SVM Based Feature Screening , 2004, MICCAI.

[15]  J. Tukey.  Comparing individual means in the analysis of variance. , 1949, Biometrics.

[16]  J. Rovamo,et al.  Michelson contrast, RMS contrast and energy of various spatial stimuli at threshold , 1993, Vision Research.

[17]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[18]  Marie Chupin,et al.  Can voxel based morphometry, manual segmentation and automated segmentation equally detect hippocampal volume differences in acute depression? , 2009, NeuroImage.

[19]  F.J. Ayres,et al.  Characterization of architectural distortion in mammograms , 2005, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[20]  Josef Kittler,et al.  Image Feature Localization by Multiple Hypothesis Testing of Gabor Features , 2008, IEEE Transactions on Image Processing.

[21]  Olivier Gevaert,et al.  Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.

[22]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[23]  Steven C. R. Williams,et al.  Measuring fMRI reliability with the intra-class correlation coefficient , 2009, NeuroImage.

[24]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[25]  Maryellen L. Giger,et al.  A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1 , 2006 .

[26]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..

[27]  K. McGraw,et al.  Forming inferences about some intraclass correlation coefficients. , 1996 .

[28]  Yung-Chang Chen,et al.  Texture features for classification of ultrasonic liver images , 1992, IEEE Trans. Medical Imaging.

[29]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[30]  E-Liang Chen,et al.  An automatic diagnostic system for CT liver image classification , 1998, IEEE Transactions on Biomedical Engineering.

[31]  David A Clausi.  An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .

[32]  E. Peli.  Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[33]  F. J. Ayres,et al.  Characterization of architectural distortion in mammograms , 2005 .

[34]  H. Greenspan,et al.  Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results. , 2010, Radiology.

[35]  T Stammberger,et al.  Interobserver reproducibility of quantitative cartilage measurements: comparison of B-spline snakes and manual segmentation. , 1999, Magnetic resonance imaging.

[36]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[37]  Tryphon Lambrou,et al.  Wavelet-Based Analysis and Classification of Liver CT , 2005 .

[38]  Saman A. Zonouz,et al.  Identification Using Encrypted Biometrics , 2013, CAIP.

[39]  Anant Madabhushi,et al.  AUTOMATED GRADING OF PROSTATE CANCER USING ARCHITECTURAL AND TEXTURAL IMAGE FEATURES , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[40]  S. Plevritis,et al.  Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features. , 2015, Radiology.

[41]  Carlo Tomasi,et al.  Texture metrics , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[42]  Akinobu Shimizu,et al.  Robust face detection using Gabor filter features , 2005, Pattern Recognit. Lett..

[43]  Xiaohong W. Gao,et al.  TEXTURE-BASED 3D IMAGE RETRIEVAL FOR MEDICAL APPLICATIONS , 2010 .

[44]  Hamid Soltanian-Zadeh,et al.  Shape-based and texture-based feature extraction for classification of microcalcifications in mammograms , 2001, SPIE Medical Imaging.

[45]  Anant Madabhushi,et al.  Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[46]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[47]  Akinobu Shimizu,et al.  Medical image analysis of 3D CT images based on extension of Haralick texture features , 2008, Comput. Medical Imaging Graph..

[48]  M. Giger,et al.  A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. , 2006, Academic radiology.

[49]  A. Ahmadian,et al.  A texture classification method for diffused liver diseases using Gabor wavelets , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[50]  Li Lu,et al.  Reliability analysis: Calculate and Compare Intra-class Correlation Coefficients (ICC) in SAS , 2007 .

[51]  Samuel H. Hawkins,et al.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. , 2014, Translational oncology.

[52]  Gilberto Zamora,et al.  Automated analysis of optic nerve images for detection and staging of papilledema. , 2011, Investigative ophthalmology & visual science.

[53]  P. Bex,et al.  Spatial frequency, phase, and the contrast of natural images. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[54]  Nikos Paragios,et al.  Automatic detection of liver tumors , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[55]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[56]  Christine P. Dancey,et al.  Statistics Without Maths for Psychology: Using Spss for Windows , 2005 .

[57]  B. Dawant,et al.  Liver segmentation in living liver transplant donors: comparison of semiautomatic and manual methods. , 2005, Radiology.

[58]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[59]  Hayit Greenspan,et al.  Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval. , 2012, Medical physics.

[60]  Jianhua Xuan,et al.  Segmentation of magnetic resonance brain image: integrating region growing and edge detection , 1995, Proceedings., International Conference on Image Processing.

[61]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[62]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[63]  Robert J. Gillies,et al.  Developing a classifier model for lung tumors in CT-scan images , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.