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http://yadda.icm.edu.pl:443/baztech/element/bwmeta1.element.baztech-d08a4793-0eee-4460-8bde-f9c17f8cf91a

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure

Autorzy Shahangian, B.  Pourghassem, H. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Brain hemorrhage is the first cause of death in ages between 15 and 24, and the third after heart diseases and cancers in other ages. Saving the lives of such patients completely depends on detecting the correct location and type of the hemorrhage in an early stage. In this paper, an automatic brain hemorrhage detection and classification algorithm on CT images is proposed. To achieve this purpose, after preprocessing, a modified version of Distance Regularized Level Set Evolution (MDRLSE) is used to detect and separate the hemorrhage regions. Then a perfect set of shape and texture features from each detected hemorrhage region are extracted. Moreover, we define a synthetic feature that is called weighted grayscale histogram feature. In this feature, valuable information from shape, position and area of the hemorrhage are integrated with the grayscale histogram of hemorrhage region. After that a synthetic feature selection algorithm is applied to select the most convenient features. Eventually, the seg- mented regions are classified into four types of the hemorrhages such as EDH, ICH, SDH and IVH by a hierarchical structure of classification. Our proposed algorithm is evaluated on a perfect set of CT-scan images and obtains the accuracy rate of 96.15%, 95.96% and 94.87% for the segmentation of the EDH, ICH, and SDH types, respectively. Also our proposed classification structure provides the accuracy rate of 92.46% and 94.13% for the first and second classifiers of the hierarchical classification structure for classifying the IVH from normal class and the EDH, ICH and SDH hemorrhage classes, respectively.
Słowa kluczowe
PL mózg   zbiór poziomicowy   klasyfikator hierarchiczny  
EN brain hemorrhage segmentation   brain hemorrhage classification   level set   weighted grayscale histogram   hierarchical classification  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2016
Tom Vol. 36, no. 1
Strony 217--232
Opis fizyczny Bibliogr. 39 poz., rys., tab., wykr.
Twórcy
autor Shahangian, B.
  • Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran
autor Pourghassem, H.
  • Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran, h_pourghasem@iaun.ac.ir
Bibliografia
[1] Cooper D, Jauch E, Flaherty M. Critical pathways for the management of stroke and intracerebral hemorrhage. Crit Pathw Cardiol 2007;6(6):18–23.
[2] Chen W, Smith R, Ji SY, Ward KR, Najarian K. Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching. BMC Med Inform Decis Mak 2009;3(November (Suppl. 1)):p1–4.
[3] Bardera A, Boada I, Feixas M, Romollo S, Blasco G, Silva Y, et al. Semi-automated method for brain hematoma and edema quantification using computed tomography. Comput Med Imaging Graph 2009;33(4):304–11.
[4] Hu Q, Qian G, Aziz A, Nowinski WL. Segmentation of brain from computed tomography head analysis. Proceedings of the 27th Annual International Conference of the IEEE-MBS Engineering in Medicine and Biology, vol. 4; 2005. p. p3375–8.
[5] Lauric A, Frisken S. Soft segmentation of CT brain data Technical report. Department of Computer Science, Tufts University; 2007.
[6] Loncaric S, Cosic D, Dhawan AP. Hierarchical segmentation of CT head images. Proceedings of the IEEE Conference on Engineering Medicine Biology, vol. 2; 1996. p. 736–7.
[7] Shahangian B, Pourghassem H. Automatic brain hemorrhage segmentation and classification in CT scan images. Proceedings of the 8th Iranian Conference on Machine Vision and Image Processing (MVIP2013); 2013. p. p467–71.
[8] Majcenic Z, Loncaric S. CT image labeling using simulated annealing algorithm’’. 9th European Signal Processing Conference (EUSIPCO); 1998. pp. 1–4.
[9] Maksimovic R, Stankovic S, Milovanovic D. Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models. Int J Med Inf 2000;58–59(September):29–37.
[10] Sacelen V, Bargelazan A. Hematoma volume detection and estimation from CT images. Acta Med Trans J 2011;16 (September (3)):298–301.
[11] Sharama B, Venugopalan K. Automatic segmentation of brain CT scan image to identify hemorrhages. Int J Comput Appl 2012;40(February (10)):1–4.
[12] Bhadauria HS, Dewal ML. Intracranial hemorrhage detection using spatial fuzzy c-mean and region-based active contour on brain CT imaging. Signal Image Video Process 2012;8(2):357–64.
[13] Mancas M, Gosselin B. Towards an automatic tumor segmentation using iterative watershed. Proceedings of Medical Imaging Conferences, vol. 5370; 2004. p. 1598–608.
[14] Qureshi AN. Semi-automated classification of CT Scans in Traumatic Brain Injury Patients. Int J Comput Appl 2015;113 (9):29–37.
[15] Wasserberg J, Mitchell B. CT scan guideline. Dept. Neurosurgery University of Birmingham; 2009, October.
[16] Liao Ch, Xiao F, Wong J, Chiang I. A multiresolution binary level set method and its application to intracranial hematoma segmentation. Comput Med Imaging Graph 2009;33(6):423–30.
[17] Sahoo PK, Soltani S, Wong AK, Chen YC. A survey of thresholding techniques. Comput Vis Graph Image Process 1988;41(2):233–60.
[18] Papamarkos N, Gatos B. A new approach for multilevel thresholds selection. Graph Models Image Process J 1994;56 (5):357–70.
[19] Chunming L, Chenyang X, Senior M. Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 2010;Vol. 19(No 12):3243–54.
[20] Ramteke R, Khachane Y. Automatic medical image classification and abnormality detection using K-nearest neighbour. Int J Adv Comput Res 2012;2(December (4)):190–6.
[21] Chih-Wei H, Chih-Jen L. A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 2002;13(March (2)):415–25.
[22] Chowdhury D, Chatterjee M, Samanta RK. An artificial neural network model for diagnosis neonatal disease. Int J Artif Intell Expert Syst (IJAE) 2011;2(3):96–106.
[23] Maduskar P, Acharyya M. Automatic identification of intracranial hemorrhage in non-contrast CT with large slice thickness for trauma cases. Proceedings of the International Society for Optical Engineering (SPIE), vol. 7260; 2009. p. 8–13.
[24] Nurmohamadi M, Pourghassem H. Clavulanic acid production estimation based on color and structural features of streptomyces clavuligerus bacteria using self-organizing map and genetic algorithm. Comput Methods Prog Biomed 2014;114(May (3)):337–48.
[25] Xu C, Prince J. Gradient vector flow: a new external force for snakes. Conference Proceedings of the Computer Vision and Pattern Recognition; 1997. p. 66–71.
[26] Nourmohamadi M, Pourghassem H. Dermoscopy image segmentation using a modified level set algorithm. Fourth International Conference on Computational Intelligence and Communication Networks (CICN2012); 2012. pp. 286–90.
[27] Sussman M, Smereka P, Osher S. A level set approach for computing solutions to incompressible two-phase flow. J Comput Phys 1994;114(September (1)):146–59.
[28] Gomes J, Faugeras O. Reconciling distance functions and level sets. J Vis Commun Image Represent 2000;11(June (2)):209–23.
[29] Rastghalam R, Pourghassem H. Breast cancer detection using MRF-based probable texture feature and decision- level fusion-based classification using HMM on thermography images. Pattern Recogn 2016;51(March): 176–89.
[30] Pourghassem H. A hierarchical logo detection and recognition algorithm using two-stage segmentation and multiple classifiers. Fourth International Conference on Computational Intelligence and Communication Networks (CICN2012); 2012. pp. 227–31.
[31] Hassanzadeh S, Pourghassem H. A fast logo recognition algorithm in noisy document images. International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI); 2011. pp. 64–7.
[32] Hassanzadeh S, Pourghassem H. Fast logo detection based on morphological features in document images. 7th IEEE Int. Colloquium on Signal Processing & Its Applications; 2011. pp. 283–6.
[33] Hassanzade S, Pourghassem H. A novel logo detection and recognition framework for separated part logos in document images. Aust J Basic Appl Sci 2011;5(September (9)):936–46.
[34] Al-Ayyoub M. Automatic detection and classification of brain hemorrhages. World Sci Eng Acad Soc (WSEAS) Trans Comput 2013;12(10):395–405.
[35] Pourghassem H, Daneshvar S. A framework for medical image retrieval using merging-based classification with dependency probability-based relevance feedback. Turk J Electr Eng Comput Sci 2013;21(3):882–96.
[36] Behnam M, Pourghassem H. Optimal query-based relevance feedback in medical image retrieval using score fusion-based classification. J Digit Imaging 2015;28(April (2)):160–78.
[37] Cheng-Huei Y, Li-Yeh Ch, Cheng-Hong Y. A hybrid filter/ wrapper method for feature selection of microarray data. J Med Biol Eng 2010;30(1):23–8.
[38] Shalikar A, Ashouri M, Nadimi Shahraki M. A CAD system for automatic classification of brain strokes in CT images. Int J Mechatron Electr Comput Technol 2014;4(10):67–85.
[39] Fesharaki NJ, Pourghassem H. Medical X-ray images classification based on shape features and bayesian rule. Fourth International Conference on Computational Intelligence and Communication Networks (CICN2012); 2012. pp. 369–73.
Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-d08a4793-0eee-4460-8bde-f9c17f8cf91a
Identyfikatory
DOI 10.1016/j.bbe.2015.12.001