Robust segmentation and intelligent decision system for cerebrovascular disease

Segmentation and classification of low-quality and noisy ultrasound images is challenging task. In this paper, a new approach is proposed for robust segmentation and classification of carotid artery ultrasound images and consequently, detecting cerebrovascular disease. The proposed technique consists of two phases, in first phase; it refines the class labels selected by user using expectation maximization algorithm. Genetic algorithm is then employed to select discriminative features based on moments of gray-level histogram. The selected features and refined targets are fed as input to neuro-fuzzy classifier for performing segmentation. Finally, intima-media thickness values are measured from segmented images to segregate the normal and abnormal subjects. In second phase, an intelligent decision-making system based on support vector machine is developed to utilize the intima-media thickness values for detecting cerebrovascular disease. The proposed robust segmentation and classification technique for ultrasound images (RSC-US) has been tested on a dataset of 300 real carotid artery ultrasound images and yields accuracy, F-measure, and MCC scores of 98.84, 0.988, 0.9767 %, respectively, using jackknife test. The segmentation and classification performance of the proposed (RSC-US) has been also tested at several noise levels and may be used as secondary observation.

[1]  Jin Young Kim,et al.  Automatic Segmentation and Decision Making of Carotid Artery Ultrasound Images , 2012, IAS.

[2]  Yuan Zhou,et al.  Ultrasound intima-media segmentation using Hough transform and dual snake model , 2012, Comput. Medical Imaging Graph..

[3]  Dl Hoyert,et al.  National Vital Statistics Reports NCHS.pdf , 2012 .

[4]  Patricia Desmond,et al.  Hippocampal and amygdala volumes according to psychosis stage and diagnosis: a magnetic resonance imaging study of chronic schizophrenia, first-episode psychosis, and ultra-high-risk individuals. , 2006, Archives of general psychiatry.

[5]  Mohammed Yeasin,et al.  Prediction of membrane proteins using split amino acid and ensemble classification , 2011, Amino Acids.

[6]  Mahmoud R. El-Sakka,et al.  Fuzzy C-Means Clustering for Segmenting Carotid Artery Ultrasound Images , 2007, ICIAR.

[7]  Wanqing Li,et al.  Semi-supervised maximum a posteriori probability segmentation of brain tissues from dual-echo magnetic resonance scans using incomplete training data , 2011 .

[8]  Sim Heng Ong,et al.  Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation , 2011, Comput. Biol. Medicine.

[9]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  C.S. Pattichis,et al.  An Integrated System for the Segmentation of Atherosclerotic Carotid Plaque , 2007, IEEE Transactions on Information Technology in Biomedicine.

[12]  S. Shirani,et al.  Segmentation of DNA microarray images using an adaptive graph-based method , 2010 .

[13]  Colin Studholme,et al.  A non-local fuzzy segmentation method: Application to brain MRI , 2011, Pattern Recognit..

[14]  Juan Morales-Sánchez,et al.  Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks , 2013, Medical & Biological Engineering & Computing.

[15]  Emmanouil G. Sifakis,et al.  Using the Hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery. , 2007, Ultrasound in medicine & biology.

[16]  L. Zadeh.  Fuzzy sets and their application to pattern classification and clustering analysis , 1996 .

[17]  Sushmita Mitra,et al.  FRBF: A Fuzzy Radial Basis Function Network , 2001, Neural Computing & Applications.

[18]  Asifullah Khan,et al.  A recent survey of reversible watermarking techniques , 2014, Inf. Sci..

[19]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[20]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[21]  Sean Borman,et al.  The Expectation Maximization Algorithm A short tutorial , 2006 .

[22]  Etienne Kerre,et al.  A new fuzzy additive noise reduction method , 2007 .

[23]  Mahmoud R. El-Sakka,et al.  Carotid Artery Ultrasound Image Segmentation Using Fuzzy Region Growing , 2005, ICIAR.

[24]  Jin Young Kim,et al.  Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering , 2012, Comput. Methods Programs Biomed..

[25]  S. Ovchinnikov.  Similarity relations, fuzzy partitions, and fuzzy orderings , 1991 .

[26]  Magdi S. Mahmoud,et al.  Expectation maximization approach to data-based fault diagnostics , 2013, Inf. Sci..

[27]  Aurélio J. C. Campilho,et al.  Segmentation of ultrasound images of the carotid using RANSAC and cubic splines , 2011, Comput. Methods Programs Biomed..

[28]  V. Khoo,et al.  The utility of multimodality imaging with CT and MRI in defining rectal tumour volumes for radiotherapy treatment planning: a pilot study , 2010, Journal of medical imaging and radiation oncology.

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

[30]  J. Goo,et al.  Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists , 2004, Korean journal of radiology.

[31]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[32]  Constantinos S. Pattichis,et al.  Texture-based classification of atherosclerotic carotid plaques , 2003, IEEE Transactions on Medical Imaging.

[33]  Jin Young Kim,et al.  Image clustering using improved spatial fuzzy C-means , 2012, ICUIMC.

[34]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Asst. Professor,et al.  Medical Image Feature , Extraction , Selection And Classification , 2022 .

[36]  N. Santhiyakumari,et al.  Medical Decision-Making System of Ultrasound Carotid Artery Intima–Media Thickness Using Neural Networks , 2011, Journal of Digital Imaging.

[37]  Tamer Ölmez,et al.  Medical image segmentation with transform and moment based features and incremental supervised neural network , 2009, Digit. Signal Process..

[38]  Colin Studholme,et al.  A non-local fuzzy segmentation method: Application to brain MRI , 2009, Pattern Recognit..

[39]  Donal B. Downey,et al.  Segmentation of carotid artery in ultrasound images , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[40]  Ulrich Anders,et al.  Model selection in neural networks , 1999, Neural Networks.

[41]  Madan M. Gupta,et al.  Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems , 2003 .

[42]  Tariq Habib Afridi,et al.  Mito-GSAAC: mitochondria prediction using genetic ensemble classifier and split amino acid composition , 2012, Amino Acids.

[43]  Mahmoud R. El-Sakka,et al.  Watershed segmentation for carotid artery ultrasound images , 2005, The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005..

[44]  Keun Ho Ryu,et al.  A data mining approach for dyslipidemia disease prediction using carotid arterial feature vectors , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[45]  Christos P. Loizou,et al.  A review of ultrasound common carotid artery image and video segmentation techniques , 2014, Medical & Biological Engineering & Computing.