

ORIGINAL ARTICLE 

Year : 2016  Volume
: 12
 Issue : 1  Page : 161168 

Measuring glioma volumes: A comparison of linear measurement based formulae with the manual image segmentation technique
Sanjeev A Sreenivasan^{1}, Venkatesh S Madhugiri^{2}, Gopalakrishnan M Sasidharan^{2}, Roopesh V.R. Kumar^{2}
^{1} Department of General Surgery, Jawaharlal Institute of Postgraduate Medical Education and Research, Gorimedu, Pondicherry, India ^{2} Department of Neurosurgery, Jawaharlal Institute of Postgraduate Medical Education and Research, Gorimedu, Pondicherry, India
Date of Web Publication  13Apr2016 
Correspondence Address: Venkatesh S Madhugiri Department of Neurosurgery, Jawaharlal Institute of Postgraduate Medical Education and Research, Gorimedu, Pondicherry  605 006 India
Source of Support: None, Conflict of Interest: None  Check 
DOI: 10.4103/09731482.153999
Context: Gliomas are irregular in shape unlike benign brain tumors like meningiomas or schwannomas. Simplifying assumptions about glioma geometry are therefore more likely to lead to wrong calculations of glioma volumes than for other tumors. Aims: We compared simple linear measurement.based techniques of measuring glioma volume with manual region of interest.based image segmentation and to assess concordance. Settings and Design: This study was a retrospective radiology archivebased study. Subjects and Methods: The volumes of gliomas were measured by two assessors using five different techniques  manual image segmentation and four linear measurementbased formulae, which included the formulae for the volume of a sphere, cylinder, ellipsoid and its simplification v = abc/2. Statistical Analysis Used: Intrassessor concordance was evaluated using mean vs. difference. (BlandAltman) plots and raw agreement indices. Interrater agreement was assessed by calculating the intraclass correlation coefficient for each technique. Results: The best inter.rater concordance was for volume measured by manual segmentation. The tumor volumes measured using the formulae for volume of a sphere and cylinder had poor agreement with the planimetric volume and low inter.rater concordance. The formula for volume of an ellipsoid and its simplification had good agreement with the manual planimetric volume and had good inter.rater concordance. However, for larger tumors, the agreement with planimetric volume was poorer. Conclusions: Manual region of interestbased image segmentation is the standard technique for measuring glioma volumes. For routine clinical use, the simple formula v = abc/2 (or the formula for volume of an ellipsoid) could be used as alternatives. Keywords: Agreement index, BlandAltman plot, glioma volume, Image J, image segmentation, intraclass correlation coefficient, volumetry
How to cite this article: Sreenivasan SA, Madhugiri VS, Sasidharan GM, Kumar RV. Measuring glioma volumes: A comparison of linear measurement based formulae with the manual image segmentation technique. J Can Res Ther 2016;12:1618 
How to cite this URL: Sreenivasan SA, Madhugiri VS, Sasidharan GM, Kumar RV. Measuring glioma volumes: A comparison of linear measurement based formulae with the manual image segmentation technique. J Can Res Ther [serial online] 2016 [cited 2019 Dec 10];12:1618. Available from: http://www.cancerjournal.net/text.asp?2016/12/1/161/153999 
> Introduction   
Gliomas are generally irregular in shape, unlike extraaxial brain tumors like meningiomas or schwannomas. They are also more heterogeneous in appearance on imaging due to the presence of areas of necrosis and infiltration. There are several clinically relevant situations where the volume of a glioma, measured accurately, reproducibly and easily, is an important piece of information to possess. Tumor volume (TumVol) is an important predictor of the extent of surgical resection (EOR).^{[1],[2]} Since the EOR correlates with overall survival, the tumor volume therefore affects survival after treatment.^{[3],[4]} The TumVol would also dictate the surgical approach  for instance, transcortical vs. transsulcal for a subcortical lesion or transcallosal vs. transcortical for an intraventricular tumor. A measurement of pretreatment TumVol is also required to assess the response to treatment. The McDonald and the RECIST criteria for evaluating tumor response to treatment both include linear measurementbased techniques for tumormetry.^{[5],[6]} Several studies have demonstrated that simple linear measurements of tumor size correlate well with the final outcome after treatment.^{[7]} However, it cannot be gainsaid that manual region of interest (ROI)based image segmentation is the gold standard for tumor volumetry against which all other techniques must be compared.
There are several simple linear measurementbased formulae that have been used to measure tumor volumes. These are more easily applied in clinical situations. The simplest is the formula for the volume of a sphere. This formula uses only a single linear measurement to estimate the volume of a tumor. A tumor seen on a stack of computerized tomography or magnetic resonance imaging (MRI) slices could also be considered to be a cylinder and in that case, its volume would be calculated by a formula that uses measurements in two orthogonal planes to estimate volumes. A better approximation would probably be to use the formula for the volume of an ellipsoid or its simplification, .^{[8]} The formula for volume of an ellipsoid as well as its simplification use linear measurements in three orthogonal planes to estimate tumor volumes. This formula has been used to measure the volumes of infarcts, traumatic and nontraumatic intracerebral hematomas as well as acoustic schwannomas.^{[5],[9],[10]}
It is important to assess how reliable these formulabased estimates are and to compare them with a standard technique. In this study, we calculated the volume of welldefined gliomas using all these techniques and compared interobserver concordance as well as agreement with the standard technique, i. e., manual ROIbased segmentation.
> Subjects and Methods   
This study was a retrospective review based on the MRI studies of all patients with supratentorial gliomas operated in our institute during the years 2010–2013. Only those MRI studies that included thin (1 or 1.5 mm) slices in the axial plane were included. For enhancing tumors, Gd contrastenhanced T1weighted sequences were used to measure volumes. For nonenhancing low grade lesions, 1 mm axial T2weighted sequences were used to measure volumes. Tumor edema is usually not present around low grade lesions and thus, T2 sequences could be easily used to measure volumes of these lesions. The volumes of the lesions were then measured separately, in a blinded manner, by two investigators  one of whom is a neurosurgeon (A1) and the other, a general surgery resident who was trained to identify tumor on MRI images (A2). Tumor volumes were measured by both assessors in five different ways as described subsequently.
Volumetry by manual ROIbased segmentation and planimetry
The image processing software Image J (National Institutes of Health, Bethesda) was used for the manual ROIbased image segmentation and volume measurement.^{[11]} The procedure and workflow are presented in [Figure 1]. The Image J interface with the ROI manager could be used to delineate tumor boundaries with a high degree of fidelity [Figure 2]a and [Figure 2]b.  Figure 1: Flowchart showing the process to estimate tumor volumes using Image J
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 Figure 2: The Image J interface. (a) – A tumor delineated using the freehand selection tool. (b) </b>– Image showing all the selected ROIs as an overlay on the tumor. (c) – HSB (hue, saturation, brightness) based tumor segmentation. The tumor is seen in red; blood in the venous sinuses, scalp and right pinna are also seen selected, (d)  The trainable Weka segmentation interface. The variously colored tissue selections were set as classes (listed on the extreme right) and used to train the segmenter. (e)  A left occipital glioblastoma. The infiltrating area (arrow) appears distinct from the main tumor (star)
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Volume by formulae
The investigators assessed the tumor on each slice of the chosen MRI sequence and selected that slice where tumor size appeared to be the largest. Further measurements were performed only on this slice. The largest measured diameter on this slice was designated a. A second diameter was measured perpendicular to a on the same slice and the largest such diameter perpendicular to a was designated b. The third (or vertical) diameter was calculated by measuring the number of slices on which tumor was seen and multiplying the number of slices by the slice thickness; this diameter was designated c.
The volume of the tumor was calculated in cubic centimeters (cc) first by using the formula for volume of a sphere (VolSph)  . To calculate the volume using the formula for volume of a cylinder, the two largest perpendicular diameters were chosen for this calculation and the volume calculated as (VolCyl) , provided a > b > c. The formula for volume of an ellipse was (VolEll) (the diameters are converted to radii).^{[8]} The tumor volume was finally calculated using the formula (VolForm) .
Analysis of intraassessor concordance of techniques with planimetric volume
The volume of each tumor measured by an assessor using manual segmentation (VolPlan) was considered the true volume for comparison. The raw tumor volumes obtained by all other techniques by an assessor were plotted against the VolPlan of the same assessor to look for scatter.
Two further tests were applied to check for agreement between VolPlan and the other techniques. The first was to generate a BlandAltman (BA; mean vs. difference) plot for each technique.^{[12]} The mean of the volumes obtained by the two techniques being compared was plotted against the difference in volumes calculated by the two techniques. The graph was generated using the software MedCalc (MedCalc for Windows, version 12.7.8.0, MedCalc Software, Ostend, Belgium). On these plots, a perfect concordance between two techniques would mean that all the plotted points would be on the line of zero difference between the volumes measured by both techniques. Acceptable discordance would be if all the points were scattered between the mean difference ± 1.96 SD. Second a raw agreement index of the volume of each tumor obtained using a particular formula with the VolPlan for that tumor was calculated. The agreement index was defined as . The AI was calculated for each tumor volume and the mean AI for each technique was calculated for assessor 1 (A1) and assessor 2 (A2). The AIs for assessor 1 were plotted against the AIs for assessor 2 on a scatter plot.^{[13]} Thus, a scatter plot of AIA1 vs. AIA2 was generated for each technique. (MS Excel, 2010).
Analysis of interassessor agreement of various techniques
Interrater agreement was assessed by calculating the intraclass correlation coefficient (ICCC) for the volumes obtained by each technique using MedCalc.^{[14]} For example, VolPlan measured by A1 (VolPlanA1) was compared with VolPlanA2 to generate an ICCC for the VolPlan technique. The ICCC was similarly calculated for each technique.
We also attempted to assess TumVol using two semiautomated techniques. This was done on Fiji, which is a software package that includes multiple plugins for Image J.^{[15]} The first technique was to use the HSB (Hue, Saturation, Brightness) technique of setting a threshold so as to select certain pixels from each slice. [Figure 2]c The difficulty with this technique is that many pixels that represent nontumor tissues on each slice also get selected, as is evident from [Figure 2]c. In this instance, the scalp, blood in the sinuses and enhancing tumor are all been selected. Thus, this technique was not included for comparison. The other technique used a preloaded plugin  the trainable Weka segmentation module [Figure 2]d. This plugin was able to select tumor areas in homogenously enhancing lesions. However, in heterogeneously enhancing tumors with areas of necrosis, this technique did not lead to high fidelity in tumor area selection and thus these results were not included in the analysis either [Figure 2]e.
Automatic and semiautomatic segmentation techniques
> Results   
Fortyfour cases were finally included in the study. Of these, 9 tumors were WHO grade 2, 20 tumors were WHO grade 3 lesions and 15 were glioblastomas (WHO grade 4).^{[16]} The mean age of the patients included was 38.9 (± 20.8) years; the youngest patient was aged 8 years and the oldest, 69 years. Total 1,605 MR image slices were evaluated for the 44 tumors by each assessor. The calculated planimetric volume of the tumors ranged between 4.7–142.2 cm ^{3}; the mean tumor volume was 44.23 cm ^{3} (± 45.64) and the median tumor volume was 26.36 cm ^{3}. The minimum, maximum and mean volume obtained by each assessor on each technique is displayed in [Table 1].  Table 1: Statistics of maximum, minimum and mean values of tumor volumes obtained by the 2 assessors using different techniques (all volumes are in cc)
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Intraassessor concordance with planimetric volume
The volume obtained by manual planimetry (VolPlan) was considered “the true volume” calculated by that assessor, and all the other formulabased techniques were compared with VolPlan. The first test for agreement that was applied was to generate the BA plots for each technique for both assessors. These were plotted first for the entire group of 44 tumors and then separately for the WHO grade 4 tumors (which form a more homogeneous subset of the 44 tumors). The plots for the whole group of 44 tumors were congruent with that for the grade 4 tumors. The plots for the WHO grade 4 tumors are displayed in [Figure 3] and [Figure 4]. The right side panels of [Figure 3] and [Figure 4]show the BA plotsfor A1 and A2, respectively. The left side panels in these figures display the raw tumor volumes plotted against VolPlan. The BA plots show that all the formulabased techniques have at least one tumor volume outside the mean difference ± 1.96 SD lines, indicating some discordance with the VolPlan. Notably, the outliers are all for lesions with high mean volumes (toward the right side of the plots), indicating that for larger tumors, the formulabased techniques are less likely to compute an accurate measurement of volume. However, it is not possible to quantify the extent of agreement of discordance between the two techniques using the BA plots.  Figure 3: Intrarater concordance of techniques for volumes of the glioblastoma tumors as assessed by assessor 1. On the left side are scatter plots of the raw tumor volumes calculated for each tumor plotted against the planimetric volume (VolPlan). The xaxis in all the rightsided graphs is the VolPlan and the y axis represents volumes obtained by the technique being compared. On the right side are the mean vs. difference (BlandAltman) plots for the corresponding technique on the right
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 Figure 4: Intrarater concordance of techniques for volumes of the glioblastoma tumors as assessed by assessor 2. On the left side are scatter plots of the raw tumor volumes calculated for each tumor plotted against the planimetric volume (VolPlan). The xaxis in all the rightsided graphs is the VolPlan and the y axis represents volumes obtained by the technique being compared. On the right side are the mean vs. difference (BlandAltman) plots for the corresponding technique on the right
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The second test of intrarater concordance of techniques was to calculate the agreement indices for each technique with the VolPlan values (of that assessor). These are displayed in [Table 2]; a perfect agreement between two techniques would mean that AI = 1. The VolEll and VolForm received the highest agreement scores with the true tumor volumes (VolPlan). The values of the AI for VolEll and VolForm were nearly identical AI for both assessors (VolEll  mean AI for A1 = 0.81, mean AI for A2 = 0.86; VolForm – mean AIA1 = 0.82, mean AIA2 = 0.86). This was expected since the formula v = abc/2 is merely a simplification and close approximation of the formula for volume of an ellipse. The scatter plots of the AI for each tumor volume measured by each technique by assessor 1 vs. the AI for assessor 2 are displayed in [Figure 5]. The points scattered at the top right corner of these plots are the tumors with the highest AIs for both assessors. It is evident that for a given observer, the TumVol calculated using the VolEll and VolForm formulae correlate best numerically with the VolPlan. Visually, the VolForm values seem to have a slightly higher AI.  Table 2: Intraassessor agreement index for the various techniques. For each assessor, the volume obtained by manual planimetry (VolPlan) was considered the gold standard
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 Figure 5: Interassessor agreement index (AI) plots. a – Plot of the agreement index for VolPlan of assessor 2 with the raw planimetric volumes calculated by assessor 1 (VolPlanA1). b, c, d – The agreement indices are calculated with respect to VolPlanA1. b – Agreement index plot for volume calculated using the formula abc/2. c  Agreement index plot for volume calculated using the formula for volume of a sphere. d  Agreement index plot for volume calculated using the formula for volume of an ellipsoid
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Interassessor concordance with planimetric volume
The ICCCs were calculated for agreement of tumor volumes obtained by a particular technique by A1 with those obtained by A2. [Table 3] As expected, planimetry received the highest ICCC of 0.966 (with a score of 1 indicating perfect concordance). Both VolEll and VolForm received identical ICCC scores (0.877).  Table 3: Intraclass correlation coefficient for interrater agreement for the various techniques. Here, each technique performed by assessor 1 is compared with the corresponding technique performed by assessor 2
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> Discussion   
The requirement to measure glioma volumes accurately arises not merely to assess response to treatment in an individual patient, but to maintain uniformity in the reporting and communication of data. Although some studies have shown that linear measurements of tumormetry compare well with volumetric methods, and indeed correlated better with survival, this conclusion appears to be counterintuitive.^{[7]} Logically, planimetry should be a better technique than any linear measurementbased tumor volume calculation, since no simplifying assumptions are made about the shape of the tumor. The limitations of manual planimetry are that it is timeconsuming and operatordependent. The thickness of the slice influences the accuracy of the result and thinner slices would mean that more time would be spent measuring the volume of each tumor. This would evidently limit the applicability of this technique in a routine clinical setting outside the purview of any clinical trial.
In contrast, linear measurements, although simple to perform, may not represent the true tumor volume and are subject to great variation depending on which slice is chosen to represent the largest tumor dimensions. Studies have shown that as many as 85% of gliomas require more than one diameter to be measured (in more than one plane) if any accurate assessment of volume (that correlated with the planimetric volume) were to be obtained.^{[8]} This study was able to establish that the technique with the best interassessor concordance was manual planimetry (ICCC = 0.966). Thus, this technique is reliable as well as reproducible across multiple assessors.
Among the linear measurementbased formula techniques, the formula for volume of an ellipse as well as the simplified version of this formula agreed equally well with the true or planimetric tumor volume. These techniques also had a good interrater concordance and the ICC scores were identical (ICCC = 0.877). It is thus evident that formulae that use either a single linear measurement or two measurements in orthogonal planes cannot adequately and accurately calculate tumor volumes for gliomas. The formula for volume of an ellipse and its simplification use linear measurements in three orthogonal planes, and thus, these three dimensional formulae agree best with the planimetric volume.
The BA plots demonstrate that the discordance between these techniques (VolEll and VolForm) and the planimetric volume is highest for the lesions with larger volumes. This could be due to the fact that larger tumors frequently have a large infiltrating front that shows “seepage” of contrast that is distinct in appearance from the main tumor. [Figure 2]e This could be due to the increased vascular permeability that is seen in high grade gliomas.^{[17]} This region could also contribute to irregularity in the shape of the tumor, making any formulabased estimates inaccurate. The volume of such lesions is ideally measured by planimetry.
Several studies have shown that automated and semiautomated techniques have good agreement with manual planimetry.^{[13],[18],[19]} Automated segmentation techniques for volumetric medical images have been based on either modelbased deformation of templates or intensity thresholding such as the region growing method.^{[20],[21]} The region growing technique is an effective approach for image segmentation of homogenous lesions.^{[20]} However, the partial volume effect limits the accuracy of MR brain image segmentation. Each voxel may represent more than one kind of tissue and this becomes problematic at tissue interfaces. Sato et al., described a modified region growing method used to remove the partial volume effects.^{[22]} However, these require the implementation of special software plugins that may not be readily available to clinicians. This is especially true when volume of gliomas is being measured since the lesions are heterogeneous in appearance. Performing semiautomated thresholdbased segmentation may not necessarily be faster than manual tracing for planimetry. A study has shown that the time taken to perform the traces in manual planimetry and to contour the tumor exactly for thresholding was not statistically different.^{[13]} The time taken for processing, however, was higher using the manual method. In this study neither the thresholding technique nor the preloaded trainable Weka segmentation could segment the tumor with an acceptable degree of fidelity. Thus, the use of such techniques outside of a trial setting may be limited.
Although gliomas are irregularly shaped tumors, this study demonstrates that if measured using a standardized technique (as we have described herein), the linear measurementbased formula for volume of an ellipse can achieve a reasonably good concordance with the true volume. The formula for the volume of an ellipse had an 87.7% concordance with the volume measured by planimetric techniques. This finding has important implications for clinical practice. Preoperative measurement of glioma volumes could be easily accomplished in a clinical setting using this simple formula. The extent of resection could also be described in terms of volume resected, in contrast to the current practice of categorizing EOR as gross total, near total, subtotal, etc. If residual tumors are treated with adjuvant therapy or postadjuvant therapy residual lesions are being followed up, a volumetric description would be much more accurate than linear dimensions. A shift to a complete volumetric description of gliomas could thus be gradually affected. This would obviously lead to more uniform communication and comparability across various clinical series. Besides this, it could lead to the evolution of a volumebased stratification of tumor resectability and prognosis in the future.
The findings of this study may have implications beyond the realm of neurooncology. The need to easily measure tumor volumes with a reasonable degree of accuracy extends to all areas of oncology. The formula for the volume of an ellipse and its simplification could form a simple desktop tool to measure tumor volumes, although studies would be required to check for agreement with standard techniques for tumors in each anatomic location.
> References   
1.  Orringer D, Lau D, Khatri S, ZamoraBerridi GJ, Zhang K, Wu C, et al. Extent of resection in patients with glioblastoma: Limiting factors, perception of resectability, and effect on survival. J Neurosurg 2012;117:8519. 
2.  Talos IF, Zou KH, OhnoMachado L, Bhagwat JG, Kikinis R, Black PM, et al. Supratentorial lowgrade glioma resectability: Statistical predictive analysis based on anatomic MR features and tumour characteristics. Radiology 2006;239:50613. 
3.  Lacroix M, AbiSaid D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, et al. A multivariate analysis of 416 patients with glioblastoma multiforme: Prognosis, extent of resection, and survival. J Neurosurg 2001;95:1908. 
4.  Laws ER, Parney IF, Huang W, Anderson F, Morris AM, Asher A, et al. Glioma Outcomes Investigators. Survival following surgery and prognostic factors for recently diagnosed malignant glioma: Data from the Glioma Outcomes Project. J Neurosurg 2003;99:46773. 
5.  Macdonald DR, Cascino TL, Schold SC Jr, Cairncross JG. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990;8:127780. 
6.  Miller AB, Hoogstraten B, Staquet M, Winkler A. Reporting results of cancer treatment. Cancer 1981;47:20714. 
7.  Shah GD, Kesari S, Xu R, Batchelor TT, O'Neill AM, Hochberg FH, et al. Comparison of linear and volumetric criteria in assessing tumour response in adult highgrade gliomas. Neuro Oncol 2006;8:3846. 
8.  Sorensen AG, Patel S, Harmath C, Bridges S, Synnott J, Sievers A, et al. Comparison of diameter and perimeter methods for tumour volume calculation. J Clin Oncol 2001;19:5517. 
9.  Luby M, Hong J, Merino JG, Lynch JK, Hsia AW, Magadan A, et al. Stroke mismatch volume with the use of ABC/2 is equivalent to planimetric stroke mismatch volume. AJNR Am J Neuroradiol 2013;34:19017. 
10.  Yu YL, Lee MS, Juan CJ, Hueng DY. Calculating the tumour volume of acoustic neuromas: Comparison of ABC/2 formula with planimetry method. Clin Neurol Neurosurg 2013;115:13714. 
11.  Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods 2012;9:6715. 
12.  Bland JM, Altman DG. Comparing methods of measurement: Why plotting difference against standard method is misleading. Lancet 1995;346:10857. 
13.  Joe BN, Fukui MB, Meltzer CC, Huang QS, Day RS, Greer PJ, et al. Brain tumour volume measurement: Comparison of manual and semiautomated methods. Radiology 1999;212:8116. 
14.  Fleiss JL, Levin B, Paik MC. The Measurement of Interrater Agreement, in Statistical Methods for Rates and Proportions, 3 ^{rd} ed. Hoboken, NJ, USA: John Wiley and Sons, Inc. 2003. doi: 10.1002/0471445428.ch18. 
15.  Schindelin J, ArgandaCarreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: An opensource platform for biologicalimage analysis. Nat Methods 2012;9:67682. 
16.  Huttner A. Overview of primary brain tumours: Pathologic classification, epidemiology, molecular biology, and prognostic markers. Hematl Oncol Clin North Am 2012;26:71532. 
17.  Jia Z, Geng D, Xie T, Zhang J, Liu Y. Quantitative analysis of neovascular permeability in glioma by dynamic contrastenhanced MR imaging. J Clin Neurosci 2012;19:8203. 
18.  Karpagam SG. Brain tumour growth and volume detection by ellipsoiddiameter technique using MRI data. Int J Comp Sci 2012;9:6. 
19.  SalmanYM. Modified technique for volumetric brain tumour measurements. J Biomed Sci Eng 2009;2:4. 
20.  Adams R. Seeded region growing. IEEE Trans Image Process 1994;16:7. 
21.  Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comp Vision 1987;1:11. 
22.  Sato ML, Wan M, Kaufman A. A gradient magnitude based region growing algorithm for accurate segmentation. Proc Intl Conf Image Process 2000;3:4. 
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3]
