Hybrid Feature Extraction and Firefly Based Feature Selection Technique for Lung Cancer Computer Aided Diagnosis
B.Mohamed fazze basha1*, Dr.M.Mohamed surputheen2
1Assistant professor,Department of computer science,Jamal Mohamed College, Tiruchirappalli- 620020, India
2Associate Professor, Department of computer science,Jamal Mohamed College, Tiruchirappalli- 620020, India
Abstrac
Lung malignant progress is one of the hazardous and life taking sickness on the globe. Be that as it may, early analysis and treatment can spare life. In spite of the statement that, CT output imaging is best imaging procedure in restorative field, it is hard for specialists to decipher and distinguish the disease from CT sweep representations. In this method PC supported conclusion can be useful for specialists to recognize the destructive cells precisely. Numerous PC helped systems consuming image preparing and AI has been looked into and executed. The fundamental point of this exploration is to assess the different PC helped procedures, examining the present best method and discovering their impediment and disadvantages lastly proposing the new model with upgrades in the present best model the new proposed model has consist a KFCM based hybrid segmentation, Hybrid feature extraction method and firefly constructed feature selection system. In the Hybrid feature extraction technique is a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM). This techniques improves the projected structure presentation in terms of accuracy. To analysis the proposed prediction and classification the future prototypical has associated with the various existing technique. The qualitative and measureable analysis shows that the proposed model provides best results compared to other models.
Keywords: CT scan imaging; Computer Aided Techniques; Discrete Wavelet Transform (DWT); Gray-Level Co-occurrence Matrix (GLCM); Lung cancer and Machine Learning.
1. Introduction
At Present, respiratory organ cancer is unique of the superior reasons of death rate global between men and women [1-2]. Although there are a portion of care choice in similar operations, like radiotherapy, and chemotherapy, in five year the natural action rate for diligent is rather less [3]. However, endurance rate may goes active up to 54% in lawsuit respiratory organ cancer is known in a primal phase [4]. Therefore, advance spotting of respiratory organ cancer is essential to decrease respiratory organ cancer impermanency. Surgical and pictorial representation method have been crucial subject in showing of respiratory organ cancer newly. CT scan becomes a common mood for detection and assessing the respiratory organ cancer [5].The Furthermost of the breathing organ nodes are ordinarily nonmalignant. However, few process may change, puffy, and sticky can also be dictated as malign. Likewise, a knotty plant process in general is malignant, but it might be reasoned as harmless example in some causes [6]. Furthermost, health check CT pictures are required to be diagnosed by tomography. Computer-aided design (CAD) group take been a vital area in surgical icon process. CAD organizations is constructed on device acquisition know-how studied to designation of Crab have become public in current years. Radioscopy and doctors may use collections of CAD systems as the instant persuasion earlier devising their personal terminal option. Therefore, CAD
arrangement show an essential function in CT scans to help radiologists for uncovering of respiratory organ cancer expeditiously. Till at this time, numerous CAD outlines have been anticipated.
For instance, Ozekes and Camurcu arranged a representing aspiratory knob detecting technique utilizing aide coordinated [7]. Schilham et al. introduced a CAD framework which have a place with picture pre-handling, applicant hub location, highlight extraction, and arrangement for knob recognition in thorax radiographs [8]. Dehmeshki et al. seen lung hub utilizing shape-based hereditary calculation format coordinating [9].
Suarez-Cuenca et al. likewise contemplated a framework which separating the knobs and non-knob cases utilizing iris channel out in CT representations [10]. Murphy et al.
precisely performed lung knob recognition utilizing k-closest neighbor mastermind [11].
Gagger et al. Complete CAD framework to distinguish lung knobs on CT pictures utilizing geometrical highlights [12]. In improver, Hasegawa et al. proposed picture process strategy for recognizable proof of lung knobs utilizing CT filters [13]. In some other examination, Kanazawa et al. utilized a CAD technique to recognize pneumonic knobs with fluffy highlights [14]. In 2005, Suzuki et al. anticipated a technique utilizing ANN for order of harmful and kind knobs on CT pictures [15]. Sun et al. analyzed help vector machines (SVM) with the some characterization techniques for recognition of lung malevolence on CT images [16]. Kuruvilla and Gunavathi planned scheme using ANNs for arrangement of lung disease [17].
2. Relating Work
Aggarwal, Furquan and Kalra [18] proposed a model that gives arrangement among knobs and typical lung life systems structure. The strategy extricates geometrical, factual and dim level qualities. LDA is utilized as classifier and ideal thresholding for division. The agenda has 84% precision, 97.14% affectability and 53.33% explicitness.
Despite the statistic that the context distinguishes the malignant growth knob, its precision is as yet unsatisfactory. No any AI methods has been exploited to characterize and straightforward division procedures is utilized. In this manner, mix of any of its means in our new model doesn't give likelihood of progress.
Jin, Zhang and Jin [19] utilized convolution neural system are classified in his CAD framework to distinguish the lung disease. The structure has 84.6% of exactness, 82.5% of affectability and 86.7% of explicitness. The benefit of this model is that it utilizes indirect channel in Region of interest (ROI) mining stage which lessens the expense of preparing and acknowledgment steps. In spite of the fact that, usage cost is diminished, it has quiet prohibited precision
Sangamithraa and Govindaraju [20] utilizes K mean unaided learning calculation for grouping or division. It bunches the pixel dataset as per certain qualities. For grouping this model actualizes back spread system. Highlights like entropy, connection, homogeneity, PSNR, SSIM are removed utilizing dim level co-event framework (GLCM) strategy. The framework has exactness of about 90.7%. Picture preprocessing middle channel is utilized for commotion expulsion which can be helpful for our new model to expel the clamor and improve the precision.
Roy, Sirohi, and Patle [21] built up a framework to identify lung disease knob utilizing fluffy obstruction framework and dynamic shape model. This framework uses dark change for picture differentiate improvement. Representation of binary is completed already division and came about picture is divided utilizing dynamic form model.
Malignant growth grouping is executed developing fluffy derivation strategy. Best part like zone, mean, entropy, relationship, significant hub length, minor pivot length are extricated to prepare the classifier. In general, exactness of the framework is 94.12%.
Checking its restriction it doesn't order the disease as amiable or dangerous which is future extent of this proposed model.
Ignatius and Joseph [22] developed a system using watershed division. In preprocessing it uses Gabor channel to recover the image quality. It differentiates the precision and neural downy model and region creating methodology. Precision of the future is 90.1% which is generally higher than the model with division using neural soft model and locale creating method. The upside of this ideal is that it uses indicator measured breaking point division which grips over division issue. As an obstruction it doesn't assemble the infection as liberal or undermining and precision is high yet simultaneously not satisfactory. A couple of changes and duty in this classic has probability of growing the precision to adequate level.
Gonzalez and Ponomaryvo [23] proposed a framework that arranges lung disease as kind or dangerous. The framework utilizes the priori data and House Field Unit (HU) to ascertain the Region of Interest (ROI). Shape highlights like zone, unpredictability, circularity, fractal measurement and textural highlights like mean, change, vitality, entropy, skinless, complexity, and smoothness are removed to prepare and characterize the help vector machine to recognize whether the knob is favorable or dangerous. The upside of this exemplary is that it characterizes disease as kindhearted or threatening.
3. Problem Statement
Cancer is growing from a half-decade and the investigation is below progress to detect lung cancer and identify them. Many techniques have emerged over a period of time but do not detect lung cancer accurately in all type of images. Some major difficulties in existing techniques are discussed as below:
Only few features has been detached for cancer nodules.
No preprocessing like clamor evacuation, picture smoothing which can likely help with expanding the recognition of knobs precisely has been actualized.
No grouping as considerate or dangerous of separated disease has been performed.
4. Proposed Scheme
To overcome this complications in this broadside for pre-processing a fresh Hybrid Laplacian of Gaussian (HLOG) filter is introduced. For lung nodes detection Modified kernel-based FCM (MKFCM) technique is introduced. The purpose of this work is to conduct the experiments through double different publicly available databases and to analyse the classification accuracy. The block diagram of the proposed system is shown in the Figure.1.
Fig. 1. Proposed system work flow.
4.1. LIDC-IBRI dataset collection
The Lung Image Database Consortium picture accumulation (LIDC-IDRI) contains of demonstrative and lung malignancy screening thoracic registered tomography (CT) checks with increased explained injuries. It is a web-open worldwide asset for improvement, preparing, and assessment of PC helped demonstrative (CAD) strategies for lung malignancy identification and conclusion. Seven scholarly focuses and eight restorative imaging organizations worked together to make this informational collection which contains 1018 cases. Each subject consolidates images from a clinical thoracic CT look at and a related XML archive that records the eventual outcomes of a two-arrange picture remark technique performed by four experienced thoracic radiologists. In the underlying blinded-read stage, every radiologist autonomously surveyed every CT examine and checked sores having a place with one of three classifications ("knob > or =3 mm," "knob <3 mm," and "non-knob > or =3 mm"). In the consequent unblinded-read stage, every radiologist autonomously checked on their own imprints alongside the anonymized signs of the three different radiologists to render a last supposition. The area of this procedure was to recognize as totally as conceivable all lung knobs in every CT filter without requiring constrained agreement. Nearly of the dataset images are shown in the below fig.2.
Fig. 2. Database sample images.
4.2. Pre-processing
Usually, the Laplacian sifts are copied filters and it is recycled for discovery the areas of limits in images. This unoriginal riddles are exact delicate to noise, so here the Gaussian filters are castoff for smoothening the image. The Gaussian filtering is accomplished before the laplacian sifting. After these two process, again the Gaussian filtering is functional to smoothen the images. So, nodes on lung district had the option to be detected all thae more exactly. Additionally, histogram sunset ready was likewise utilized in upgrade step so as to limit differentiate contrasts which happen because of checking blunders and to evacuate unneeded grains. The filtering of LOG is revealed in the equation (1) and the Gaussian filtering of HLOG is presented in equation (2).
The HLoG scale space representation is
(1) (2)
Some of the pre-pressed images are shown in the below figure. Which are Gaussian noise removed and enhanced by the laplacian filter.
Fig. 2. Database sample images.
4.3. Lung-volume extraction and segmentation
The segmentation process of mammogram can be efficiently performed, so that digitization noise and high-frequency components are some of the unwanted regions present in the mastography images that are removed using filtering. Identification of normal and abnormal is critical to accurately extract the features. In this proposed metrology to extract the lung volume morphological operations are considered based on this volume extracted images the cancer regions are segment. The Lung regions can be identified using segmentation techniques. The proposed system consider Modified kernel-based FCM framework is denoted as MKFCM. In this system first, we calculate the Flexible parameter associated with every pixel to control the contextual information using (3). The objective function is defined as
(3) The minimization of can be calculated through an alternate optimization procedure using
(4) (5) When is replaced with the grayscale of the average filter of the input image, the algorithm is denoted as MKFCM. When is replaced with the weight image defined. The algorithm is denoted as . The main step for the MKFCM is mentioned below:
MKFCM algorithm
(1) Initialized threshold loop counter t (2) Calculate the Flexible regularization parameter
(3) Calculate for MKFCM
(4) Calculate cluster centres using as in (5).
(5) Calculate the membership function within (4)
(6) If max then stop; otherwise, update and go to step
(4).
4.4. Feature extraction (Hybrid feature extraction)
After segmentation process, highlights pulling out is a significant advance in any grouping issue. Highlights contain applicable data required to recognize various classes.
Surface properties of a picture container can be used for order reason. Surface contains data about the auxiliary disposed strategy of surfaces in a depiction. In this work, the underlying phase of the component extraction is wavelet highlights are extricated from each sectioned pictures. With the assistance of wavelet highlights Gray Level Co-event Matrix (GLCM) is applied and the element esteems are extricated. Highlights register's such us, Autocorrelation, Contrast, Correlation, Correlation, Cluster Prominence, Cluster Shade, Dissimilarity, Energy Entropy, Homogeneity, Homogeneity, Maximum likelihood, Amount of cubes, Variance, Sum normal, Sum change, Sum entropy, Difference fluctuation, Transformation entropy, Evidence proportion of correlation1, Information proportion of correlation2, Inverse distinction, Inverse contrast standardized and Converse alteration moment normalized [24].
4.5. Firefly based Feature selection Copyright Form
Feature Selection (FS) and decrease of example dimensionality is a most significant advance in example acknowledgment frameworks. In this framework Firefly Optimization Algorithm for Feature Selection is utilized the proposed Firefly variation utilizes the Logistic confused guide for populace instatement to build swarm decent variety. In the wake of positioning all fireflies as per their wellness esteems, the worldwide best arrangement, g_best is recovered. So as to build search assorted variety and defeat the nearby ideal snares, we likewise recognize a second swarm pioneer, g_best in every cycle. This optional swarm pioneer has a practically identical wellness esteem yet with low relationship in situation to the best chief. Since both swarm pioneers are bound to investigate unmistakable inquiry areas, this proposed system diminishes the probability of being caught in nearby optima. In addition, the ideal posterity of the mean situation of the two heads and the adjoining more brilliant arrangements are utilized to direct the proposed engaging quality pursuit activity and lead the fireflies with lesser bright forces to travel to the ideal districts. Conditions (6)- (8) describe the proposed engaging quality expedition activity.
(6) (7) (8) Where represents the fitter offspring of the brighter neighbouring firefly identified by SA as defined in Equation (7), whereas denotes the fitter offspring of the mean of two swarm leaders generated by SA as defined in Equation (8). Denotes each employed chaotic map, while indicates a randomized vector. Moreover, we use an adaptive step parameter defined as , with representing the maximum number of iterations. As such, the search process utilizes a larger setting at the initial iterations to increase diversity of the solution vectorsand a smaller setting at the final iterations to perform fine-tuning
4.6. Classification and Random Forest
The Random Forest (RF) classifier is set of de trees, transformed into an outstanding decision in the AI structures. Extended usage of sporadic timberland can be seen in computational science field, inferable from its good conditions in overseeing obliged model size, complex data structures and multidimensional component space.
Sporadic forest uses different free decision trees which are made by self-assertively picked variables. The free trees are worked by a figuring. After trees are made they vote to find the most conspicuous class. The figuring guarantees that all trees in the forested areas are extraordinary. Intervention is applied in two phases. Introductory advance is to use particular bootstrap test data to fabricate each tree. Second step is to pick a subset of markers aimlessly then separating each center point of trees with the best subset as opposite to all pointers. There are two inspirations to have bootstrap step. Introductory one is gathering precision increment when self-assertive features are used and the ensuing one is to decrease theory bungle. Subjective assurance of separating indicates improvement over pressing the extent that hypothesis botch. The nature of the individual tree classifiers is huge on course of action. At times, this computation works better than some various classifiers, for instance, support vector machines, neural frameworks and discriminant examination. Regardless of the way that unpredictable forest was presented as decision trees, it can work with various classifiers. In this assessment, the perfect number of trees portrayed as 5 for capable results [25].
5. Results and Discussion
The proposed system is experimented using MATLAB (version 2018a) with 3.0 GHz Intel i3 processor, 1TB hard disc and 8 GB RAM. For determining the effectiveness of the proposed system is compared with the existing systems on the publically available datasets LIDC-IBRI. In the proposed system, in LIDC-IBRI dataset 240 images are trained and 90 images are tested.
5.1. Evaluation Metrics
The challenge evaluation metrics is used for evaluating the both segmentation and classification performance of our method. For the segmentation, the evaluation criteria include sensitivity (SE), specificity (SP), accuracy (AC), Recall (R) and Precision (P).
The performance criteria are defined is as:
fn tp SE tp
fn tn fp tp
tn AC tp
fn fp tn tp
tn P tp
fp tn SP tn
Where and denote the number of a true positive, true negative, false positive and false negative. As for the classification, there are four evaluation criteria, including sensitivity (SE), specificity (SP), accuracy (AC) Recall (R) and Precision (P).
5.2. Performances analysis
The proposed system performance has validated in various ways such, Qualitative and Quantitative which are discussed in the below section.
A. Qualitative Analysis
In this section, the proposed system has analyzed in various ways such us with optimization, without optimization and various classification iterations. To validate and comparison purpose, the proposed system is compared with Support Vector Machine
Tab.1.Comparision of optimization and without optimization.
Technique AC SE SP P R
SVM Without Optimization
66.6666 0 0 100 0
KNN Without Optimization
77.7777 56.6666 88.3333 70.8333 56.6666
Proposed RF Without Optimization
88.8888 90.0000 88.3333 80.1530 90.0000
SVM With Optimization
33.3333 100 0 33.3333 100
KNN With optimization
31.1111 93.33333 0 31.8181 93.3333 Proposed RF
With optimization
97.7777 93.3333 100 100 93.3333
From the analysis table.1. Shows that comparison among the width optimization and without optimization. In this without feature optimization Proposed RF provides 88.88 % accuracy, similarly KNN provides 77.77 % and SVM provides 66.66
%.compared to other classifiers proposed provides much better results. But the results are not enough so that the firefly optimization based feature selection algorithm is applied from that Proposed RF provides 97.77 %.
B. Quantitative Analysis
In this section, the proposed system has analyzed and compared with various existing systems such as Aggarwal, Furquan and Kalra [18], Jin, Zhang and Jin [19], Sangamithraa and Govindaraju [20], Roy, Sirohi, and Patle [21] and Ignatious and Joseph [22]. Which all are detailed in the section.2. The comparison is defined in the table.2. and fig.4.
Tab.2.Comparative analysis.
AC SE SP
Aggarwal, Furquan and Kalra [18]
84.0% 97.14% 53.33%
Jin, Zhang and Jin [19] 84.6% 82.5% 86.7%
Sangamithraa and Govindaraju [20]
90.7% -- --
Roy, Sirohi, and Patle [21]
94.12% -- --
Ignatious and Joseph [22]
90.1% -- --
Proposed RF With optimization
97.77% 93.33% 100%
Fig.4.Proposed comparative analysis.
6. Conclusion
The present best model has no acceptable consequence of precision and doesn't group level of malignant growth of identified knobs. Hence new framework is proposed.
The proposed framework is utilized to distinguish the dangerous knob from the lung CT sweep picture utilizing watershed division for location and RF for grouping of knob as glioma, Meningioma and pituitary tumor. Proposed model distinguishes the malignant growth with 97% exactness which is higher than current model and classifier has precision of 94.12%. By and large, we can see improvement in the proposed framework in contrast with current best model However, this proposed doesn't arranges into various stages as stage I, II, III of malignancy. In this way, as future extension improvement in this should be possible by actualizing order in various stages. Likewise, further exactness can be expanded by legitimate pre-handling and ends of false articles.
References
[1] R. Siegel, D. Naishadham, and A. Jemal, “Cancer statistics,” CA: A Cancer Journal for Clinicians, vol. 62, no. 1, pp. 10–29, 2012.
[2] C. Jacobs, E. M. Van Rikxoort, T. Twellmann et al., “Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images,” Medical Image Analysis, vol. 18, no. 2, pp.
374–84, 2014.
[3] E. Dandil, M. Cakiroglu, Z. Eksi, M. Ozkan, O. K. Kurt, and A. Canan, “Artificial neural network- based classification system for lung nodules on computed tomography scans,” in Proceedings of 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 382–
86, Tunis, Tunisia, August 2014
[4] N. Howlader, A. Noone, M. Krapcho et al., SEER Cancer Statistics Review, 1975-2011, National Cancer Institute, Bethesda, MD, USA, 2014.
[5] H. Han, L. Li, F. Han, B. Song, W. Moore, and Z. Liang, “Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 2, pp. 648–659, 2015.
[6] Y. J. Jeong, C. A. Yi, and K. S. Lee, “Solitary pulmonary nodules: detection, characterization, and guidance for further diagnostic workup and treatment,” American Journal of Roentgenology, vol.
188, no. 1, pp. 57–68, 2007.
[7] S. Ozekes and A. Y. Camurcu, Automatic Lung Nodule Detection Using Template Matching, Springer, Berlin, Germany, 2006.
[8] A. M. Schilham, B. Van Ginneken, and M. Loog, “A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database,” Medical Image Analysis, vol. 10, no. 2, pp. 247–58, 2006.
[9] J. Dehmeshki, X. Ye, X. Lin, M. Valdivieso, and H. Amin, “Automated detection of lung nodules in CT images using shape-based genetic algorithm,” Computerized Medical Imaging and Graphics, vol. 31, no. 6, pp. 408–17, 2007.
[10] J. J. Suarez-Cuenca, P. G. Tahoces, M. Souto et al., “Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images,” Computers in Biology and Medicin, vol. 39, no. 10, pp. 921–33, 2009.
[11] K. Murphy, B. Van Ginneken, A. M. Schilham, B. J. De Hoop, H. A. Gietema, and M. Prokop, “A large-scale evaluation of automatic pulmonary nodule detection in chest CTusing local image features and k-nearest-neighbour classification,” Medical Image Analysis, vol. 13, no. 5, pp. 757–
70, 2009.
[12] M. L. Giger, K. T. Bae, and H. Macmahon, “Computerized detection of pulmonary nodules in computed tomography images,” Investigative Radiology, vol. 29, no. 4, pp. 459–65, 1994.
[13] J. Hasegawa, K. Mori, J. Toriwaki, H. Anno, and K. Katada, “Automated extraction of lung cancer lesions from multi-slice chest CT images by using three-dimensional image processing,” Systems and Computers in Japan, vol. 25, no. 11, pp. 68–77, 1994.
[14] K. Kanazawa, Y. Kawata, N. Niki et al., “Computer-aided diagnosis for pulmonary nodules based on helical CT images,” Computerized Medical Imaging and Graphics, vol. 22, no. 2, pp. 157–67, 1998.
[15] K. Suzuki, F. Li, S. Sone, and K. Doi, “Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network,” IEEE Transactions on Medical Imaging, vol. 24, no. 9, pp. 1138–50, 2005.
[16] T. Sun, J. Wang, X. Li et al., “Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set,” Computer Methods and Programs in Biomedicine, vol. 111, no. 2, pp. 519–24, 2013.
[17] J. Kuruvilla and K. Gunavathi, “Lung cancer classification using neural networks for CTimages,”
Computer Methods and Programs in Biomedicine, vol. 113, no. 1, pp. 202–9, 2014.
[18] Aggarwal, T., Furqan, A., & Kalra, K. (2015) “Feature extraction and LDA based classification of lung nodules in chest CT scan images.” 2015 International Conference On Advances In Computing, Communications And Informatics (ICACCI), DOI: 10.1109/ICACCI.2015.7275773.
[19] Jin, X., Zhang, Y., & Jin, Q. (2016) “Pulmonary Nodule Detection Based on CT Images Using Convolution Neural Network.” 2016 9Th International Symposium On Computational Intelligence And Design (ISCID). DOI: 10.1109/ISCID.2016.1053.
[20] Sangamithraa, P., & Govindaraju, S. (2016) “Lung tumour detection and classification using EK- Mean clustering.” 2016 International Conference On Wireless Communications, Signal Processing And Networking (Wispnet). DOI: 10.1109/WiSPNET.2016.7566533.
[21] Roy, T., Sirohi, N., & Patle, A. (2015) “Classification of lung image and nodule detection using fuzzy inference system.” International Conference On Computing, Communication & Automation.
DOI: 10.1109/CCAA.2015.7148560.
[22] Ignatious, S., & Joseph, R. (2015) “Computer aided lung cancer detection system.” 2015 Global Conference On Communication Technologies (GCCT), DOI: 10.1109/GCCT.2015.7342723.
[23] Rendon-Gonzalez, E., & Ponomaryov, V. (2016) “Automatic Lung nodule segmentation and classification in CT images based on SVM.” 2016 9Th International Kharkiv Symposium On Physics And Engineering Of Microwaves, Millimeter And Submillimeter Waves (MSMW). DOI:
10.1109/MSMW.2016.7537995.
[24] D A. Clausi, An analysis of co-occurrence texture statistics as a function of grey level quantization, Can. J. Remote Sensing, vol. 28, no. 1, pp. 45-62, 2002.
[25] Gokgoz, E. and Subasi, A., 2015. Comparison of decision tree algorithms for EMG signal classification using DWT. Biomedical Signal Processing and Control, 18, pp.138-144.
