Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease

Lung segmentation in high-resolution computed tomography (HRCT) images is necessary before the computer-aided diagnosis (CAD) of interstitial lung disease (ILD). Traditional methods are less intelligent and have lower accuracy of segmentation. This paper develops a novel automatic segmentation model using radiomics with a combination of hand-crafted features and deep features. The study uses ILD Database-MedGIFT from 128 patients with 108 annotated image series and selects 1946 regions of interest (ROI) of lung tissue patterns for training and testing. First, images are denoised by Wiener filter. Then, segmentation is performed by fusion of features that are extracted from the gray-level co-occurrence matrix (GLCM) which is a classic texture analysis method and U-Net which is a standard convolutional neural network (CNN). The final experiment result for segmentation in terms of dice similarity coefficient (DSC) is 89.42%, which is comparable to the state-of-the-art methods. The training performance shows the effectiveness for a combination of texture and deep radiomics features in lung segmentation.

[1]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[2]  Qi Tian,et al.  A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise , 2017, PloS one.

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

[4]  Zhou Jianzheng.  New objectionable sounds recognition method , 2013 .

[5]  Niranjan Khandelwal,et al.  Segmentation of interstitial lung disease patterns in HRCT images , 2015, Medical Imaging.

[6]  D. Mollura,et al.  Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends. , 2015, Radiographics : a review publication of the Radiological Society of North America, Inc.

[7]  Josef Kittler,et al.  Region growing: a new approach , 1998, IEEE Trans. Image Process..

[8]  Sabina Sonia Tangaro,et al.  Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar Region , 2011, Journal of Digital Imaging.

[9]  Minghua Zhao,et al.  [ARTICLE WITHDRAWN] Novel Method Using Multiple Strategies for Accurate Lung Segmentation in Computed Tomography Images , 2016 .

[10]  Antoine Geissbühler,et al.  Building a reference multimedia database for interstitial lung diseases , 2012, Comput. Medical Imaging Graph..

[11]  Yannick Le Moullec,et al.  Methods for increased sensitivity and scope in automatic segmentation and detection of lung nodules in CT images , 2015, 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[12]  Shuiping Gou,et al.  CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition , 2013, PloS one.

[13]  Tuan D. Pham,et al.  Estimating Parameters of Optimal Average and Adaptive Wiener Filters for Image Restoration with Sequential Gaussian Simulation , 2015, IEEE Signal Processing Letters.

[14]  Guowei Yang,et al.  Feature extraction using dual-tree complex wavelet transform and gray level co-occurrence matrix , 2016, Neurocomputing.

[15]  Tao Gong,et al.  An Effective Hybrid Windowed Fourier Filtering and Fuzzy C-Mean for Pulmonary Nodule Segmentation , 2018 .

[16]  Qaisar Abbas,et al.  Segmentation of differential structures on computed tomography images for diagnosis lung-related diseases , 2017, Biomed. Signal Process. Control..

[17]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[18]  V. Vasconcelos,et al.  Enhanced Classification of Interstitial Lung Disease Patterns in HRCT Images Using Differential Lacunarity , 2015, BioMed research international.

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

[20]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[21]  Javad Alirezaie,et al.  Lung Segmentation in Pulmonary CT Images using Wavelet Transform , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[22]  Ammi Reddy Pulagam,et al.  Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases , 2016, Journal of Digital Imaging.

[23]  Shahrokh Valaee,et al.  Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks , 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  Luca Richeldi,et al.  Interstitial lung diseases. Preface. , 2012, Clinics in chest medicine.

[25]  He Jianfeng.  Image denoising method based on BEMD and adaptive Wiener filter , 2013 .

[26]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Banshidhar Majhi,et al.  Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer , 2015, Neurocomputing.

[28]  A. Russell,et al.  Detecting anxiety and depression in patients diagnosed with an interstitial lung disease. Can we do better? , 2014, Respirology.

[29]  Paulo Mazzoncini de Azevedo Marques,et al.  Radiomics-based features for pattern recognition of lung cancer histopathology and metastases , 2018, Comput. Methods Programs Biomed..

[30]  Elena Casiraghi,et al.  A Fully Automated Method for Lung Nodule Detection From Postero-Anterior Chest Radiographs , 2006, IEEE Transactions on Medical Imaging.

[31]  Bram van Ginneken,et al.  Automatic Segmentation of Pulmonary Segments From Volumetric Chest CT Scans , 2009, IEEE Transactions on Medical Imaging.

[32]  Baihua Li,et al.  A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis , 2017, Journal of Medical Systems.

[33]  Simon X. Yang,et al.  Adaptive neuro-fuzzy inference systems based approach to nonlinear noise cancellation for images , 2007, Fuzzy Sets Syst..

[34]  Minghua Zhao,et al.  Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images , 2016, BioMed research international.

[35]  Bram van Ginneken,et al.  Computer analysis of computed tomography scans of the lung: a survey , 2006, IEEE Transactions on Medical Imaging.

[36]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[37]  Ayman El-Baz,et al.  Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling , 2017, IEEE Transactions on Medical Imaging.

[38]  T. Barbu.  Variational Image Denoising Approach with Diffusion Porous Media Flow , 2013 .

[39]  Jonathan G Goldin,et al.  High-resolution CT scan findings in patients with symptomatic scleroderma-related interstitial lung disease. , 2008, Chest.

[40]  Jun Zhang,et al.  Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics , 2019, IEEE Transactions on Medical Imaging.

[41]  A. R. Amanda,et al.  Comparison of image segmentation of lungs using methods: connected threshold, neighborhood connected, and threshold level set segmentation , 2016 .