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Current Bioinformatics


ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

Author(s): Yu Wang, Fuqian Shi*, Luying Cao, Nilanjan Dey, Qun Wu, Amira Salah Ashour, Robert Simon Sherratt, Venkatesan Rajinikanth and Lijun Wu

Volume 14, Issue 4, 2019

Page: [282 - 294] Pages: 13

DOI: 10.2174/1574893614666190304125221

Price: $65


Background: To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed.

Objective: For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery.

Methods: In the current work, entropy-based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images.

Results: The segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be accurate when only trained by 8 GLCMs.

Conclusion: The research illustrated that the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision.

Keywords: Morphological segmentation, top-hat transformation, threshold based Watershed segmentation, texture feature extraction, mice liver fibrosis, microscopic images, support vector machine.

Graphical Abstract
Jiang W, Yin Z. Seeing the invisible in differential interference contrast microscopy images. Med Image Anal 2016; 34: 65-81.
Dey N, Ashour AS, Ashour AS, Singh A. Digital analysis of microscopic images in medicine. J Adv Micro Res 2015; 10: 1-13.
Dey N, Ashour AS, Chakraborty S, et al. Healthy and unhealthy rat hippocampus cells classification: a neural based automated system for Alzheimer disease classification. J Adv Micro Res 2016; 11: 1-10.
Kotyk T, Dey N, Ashour AS, et al. Detection of dead stained microscopic cells based on color intensity and contrast.Paper presented at the First International Conference on Advanced Intelligent Systems and Informatics, AISI2015, 28-30 Nov 2015. Beni Suef, Egypt. Springer velarg 2015.
Rakotomamonjy A, Petitjean C, Salaün M, Thiberville L. Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images. Artif Intell Med 2014; 61(2): 105-18.
Tahir M, Khan A. Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification. Inf Sci 2016; 345: 65-80.
Nellros F, Thurley MJ, Jonsson H, Andersson C, Forsmo SPE. Automated measurement of sintering degree in optical microscopy through image analysis of particle joins. Pattern Recognit 2015; 48: 3451-65.
Ashour AS, Beagum S, Dey N, et al. Light microscopy image de-noising using optimized LPA-ICI filter article/10.1007%2Fs00521-016-2678-9
Chun MG, Kong SG. Focusing in thermal imagery using morphological gradient operator. Pattern Recognit Lett 2014; 38: 20-5.
Zia S, Jaffar MA, Choi TS. Morphological gradient based adapted selective filter for removal of Rician noise from magnetic resonance images. Microsc Res Tech 2012; 75(8): 1044-50.
Li B, Zhang PL, Mi SS, Hu RX, Liu DS. An adaptive morphological gradient lifting wavelet for detecting bearing defects. Mech Syst Signal Process 2012; 29: 415-27.
Khakipour MH, Safavi AA, Setoodeh P. Bearing fault diagnosis with morphological gradient wavelet. J Franklin Inst 2016; 354: 2465-76.
Dorini FA, Dorini LB, Lesinhovski WC. A mathematical analysis of the tensorial morphological gradient approach. Pattern Recognit Lett 2015; 68: 97-102.
Li H, Li L, Zhang J. Multi-focus image fusion based on sparse feature matrix decomposition and morphological filtering. Opt Commun 2015; 342: 1-11.
Bai X. Morphological center operator based infrared and visible image fusion through correlation coefficient. Infrared Phys Technol 2016; 76: 546-54.
Farihan A, Raffei M, Asmuni H, Hassan R, Othman RM. Frame detection using gradients fuzzy logic and morphological processing for distant color eye images in an intelligent iris recognition system. Appl Soft Comput 2015; 37: 363-81.
Gelzinis A, Verikas A, Vaiciukynas E, et al. Automatic detection and morphological delineation of bacteriophages in electron microscopy images. Comput Biol Med 2015; 64: 101-16.
Preziosi BM, Bowden TJ. Morphological characterization via light and electron microscopy of Atlantic jackknife clam (Ensis directus) hemocytes. Micron 2016; 84: 96-106.
Oschatz M, Pré P, Dörfler S, et al. Nanostructure characterization of carbide-derived carbons by morphological analysis of transmission electron microscopy images combined with physisorption and Raman spectroscopy. Carbon 2016; 105: 314-22.
Kayasandik CB, Labate D. Improved detection of soma location and morphology in fluorescence microscopy images of neurons. J Neurosci Methods 2016; 274: 61-70.
Yamamoto S, Oshima Y, Saitou T, et al. Quantitative imaging of fibrotic and morphological changes in liver of non-alcoholic steatohepatitis (NASH) model mice by second harmonic generation (SHG) and auto-fluorescence (AF) imaging using two-photon excitation microscopy (TPEM). Biochem Biophys Rep 2016; 8: 277-83.
López-Mir F, Naranjo V, Angulo J, Alcañiz M, Luna L. Liver segmentation in MRI: A fully automatic method based on stochastic partitions. Comput Methods Programs Biomed 2014; 114(1): 11-28.
Das A, Ghoshal D. Human skin region segmentation based on chrominance component using modified watershed algorithm. Procedia Comput Sci 2016; 89: 856-63.
Wong AKO, Hummel K, Moore C, et al. Improving reliability of pQCT-derived muscle area and density measures using a watershed algorithm for muscle and fat segmentation. J Clin Densitom 2015; 18(1): 93-101.
Masoumi H, Behrad A, Pourmina MA, Roosta A. Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network. Biomed Signal Process Control 2012; 7: 429-37.
Wieclawek W, Pietka E. Watershed based intelligent scissors. Comput Med Imaging Graph 2015; 43: 122-9.
Gatiatulina ER, Popova EV, Polyakova VS, et al. Evaluation of tissue metal and trace element content in a rat model of non-alcoholic fatty liver disease using ICP-DRC-MS. J Trace Elem Med Biol 2017; 39: 91-9.
Hore S, Chakroborty S, Ashour AS, et al. Finding contours of hippocampus brain cell using microscopic image analysis. J Adv Micro Res 2015; 10: 93-103.
Li CH, Ge XL, Pan K, Wang PF, Su YN, Zhang AQ. Laser speckle contrast imaging and Oxygen to See for assessing microcirculatory liver blood flow changes following different volumes of hepatectomy. Microvasc Res 2017; 110: 14-23.
Vreuls CPH, Driessen A, Olde Damink SW, et al. Sinusoidal obstruction syndrome (SOS): A light and electron microscopy study in human liver. Micron 2016; 84: 17-22.
Sayed GI, Hassanien AE, Schaefer G. An automated computer-aided diagnosis system for abdominal CT liver images. Procedia Comput Sci 2016; 90: 60-73.
Li W. Design and implementation of CAD system based on multiphase liver images, PhD Thesis, Shanghai Jiaotong¶ University, 2010.
Theodoridis S, Koutroumbas K. Pattern Recognition. Beijing, China: Publishing House of Electronics Industry 2000.
Chang HH, Zhuang AH, Valentino DJ, Chu WC. Performance measure characterization for evaluating neuroimage segmentation algorithms. Neuroimage 2009; 47(1): 122-35.

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