A fast weak-supervised pulmonary nodule segmentation method based on modified self-adaptive FCM algorithm

One of the key problems of computer-aided diagnosis is to segment specific anatomy structures in tomographic images as fast and accurately as possible, which is an important step toward identifying pathologically changed tissues. The segmentation accuracy has a significant impact on diseases diagnosis as well as the therapeutic efficacy. This paper presents a fast and robust weak-supervised pulmonary nodule segmentation method based on a modified self-adaptive FCM algorithm. To improve the traditional FCM, we firstly introduce an enhanced objective function, which computes the membership value according to both the grayscale similarity and spatial similarity between central pixels and neighbors. Then, a probability relation matrix between clusters and categories is constructed by using a small amount of prior knowledge learned from training samples. Based on this matrix, we realize a weak-supervised pulmonary nodules segmentation for unlabeled lung CT images. More specifically, the proposed method utilizes the relation matrix to calculate the category index of every pixel by Bayesian theory and PSOm algorithm. The quantitative experimental results on a test dataset, including 115 2-D clinical CT data, demonstrate the accuracy, efficiency and generality of the proposed weak-supervised strategy in pulmonary nodules segmentation.

[1]  Marcelo Gattass,et al.  Automatic segmentation of lung nodules with growing neural gas and support vector machine , 2012, Comput. Biol. Medicine.

[2]  Ling Shao,et al.  A rapid learning algorithm for vehicle classification , 2015, Inf. Sci..

[3]  Caiming Zhang,et al.  Medical image segmentation using improved FCM , 2012, Science China Information Sciences.

[4]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[5]  Jin Li,et al.  Secure Deduplication with Efficient and Reliable Convergent Key Management , 2014, IEEE Transactions on Parallel and Distributed Systems.

[6]  Antoni B. Chan,et al.  On measuring the change in size of pulmonary nodules , 2006, IEEE Transactions on Medical Imaging.

[7]  Yu-an Tan,et al.  An Effective RAID Data Layout for Object-Based De-duplication Backup System , 2016 .

[8]  Fei Wu,et al.  Determining Image Base of Firmware Files for ARM Devices , 2016, IEICE Trans. Inf. Syst..

[9]  Caiming Zhang,et al.  Patch Grouping SVD-Based Denoising Aggregation Patch Grouping SVD-Based Denoising Aggregation Back Projection Noisy Image , 2015 .

[10]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[11]  Bram van Ginneken,et al.  A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification , 2009, Medical Image Anal..

[12]  V. Vijaya Kishore,et al.  Performance evaluation of edge detectors - morphology based ROI segmentation and nodule detection from DICOM lung images in the noisy environment , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[13]  S.M. Szilagyi,et al.  MR brain image segmentation using an enhanced fuzzy C-means algorithm , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[14]  Marcos Salganicoff,et al.  Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models , 2011, Medical Image Anal..

[15]  Xiaoou Tang,et al.  Tensor linear Laplacian discrimination (TLLD) for feature extraction , 2009, Pattern Recognit..

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

[17]  Stelios Krinidis,et al.  A Robust Fuzzy Local Information C-Means Clustering Algorithm , 2010, IEEE Transactions on Image Processing.

[18]  Caiming Zhang,et al.  Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification , 2015, Comput. Math. Methods Medicine.

[19]  Jamshid Dehmeshki,et al.  Automated detection of lung nodules in CT images using shape-based genetic algorithm , 2007, Comput. Medical Imaging Graph..

[20]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[21]  Andrés Ortiz,et al.  Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering , 2014, Inf. Sci..

[22]  Li Guangyao,et al.  Geometric active contours without re-initialization for image segmentation , 2009 .

[23]  Jin Li,et al.  Securely Outsourcing Attribute-Based Encryption with Checkability , 2014, IEEE Transactions on Parallel and Distributed Systems.

[24]  Cong Wang,et al.  Efficient verifiable fuzzy keyword search over encrypted data in cloud computing , 2013, Comput. Sci. Inf. Syst..

[25]  Hans-Ulrich Kauczor,et al.  Can lung MR replace lung CT? , 2008, Pediatric Radiology.

[26]  Ghassan Hamarneh,et al.  Medial-Based Deformable Models in Nonconvex Shape-Spaces for Medical Image Segmentation , 2012, IEEE Transactions on Medical Imaging.

[27]  J. Bezdek.  A Physical Interpretation of Fuzzy ISODATA , 1993 .

[28]  Shoji Kido,et al.  Automatic segmentation of pulmonary nodules on CT images by use of NCI lung image database consortium , 2006, SPIE Medical Imaging.

[29]  K. Doi,et al.  Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. , 2008, Academic Radiology.

[30]  Jin Li,et al.  Identity-Based Encryption with Outsourced Revocation in Cloud Computing , 2015, IEEE Transactions on Computers.

[31]  Naixue Xiong,et al.  Steganalysis of LSB matching using differences between nonadjacent pixels , 2016, Multimedia Tools and Applications.

[32]  Hidenori Itoh,et al.  Lung Segmentation in Chest Radiographs by Means of Gaussian Kernel-Based FCM with Spatial Constraints , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[33]  Mohamad Amin Bakhshali.  Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory , 2017, Soft Comput..

[34]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[35]  Fangfang Dong,et al.  Brain MR image segmentation based on local Gaussian mixture model and nonlocal spatial regularization , 2014, J. Vis. Commun. Image Represent..

[36]  A. R. Ramli,et al.  Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[37]  Tzong-Jer Chen,et al.  Fuzzy c-means clustering with spatial information for image segmentation , 2006, Comput. Medical Imaging Graph..

[38]  Bin Gu,et al.  A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[39]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.