Application program for wound healing measurement based on the region growing technique: A trial version

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Suratchanee Phadngam
J Ruthiang
N Ina
A Ferraresi
C Isidoro


Background: The study of cell migration is one of the most interesting topics in cancer research. Wound healing assay is a basic 2D technique used to follow cell migration. The area of a wound’s images are measured and calculated to demonstrate the percentage of moving cells. Normally, the measurements are estimated by direct scaling of observers, which unavoidably involves human error. Moreover, there is yet to be a gold standard technique for measurement. Recently, image processing tools have been provided to use and easily carry out measurement, such as ImageJ software and others.

Objectives: This study proposed to take the advantage of the region growing algorithm for wound healing measurement.

Materials and methods: Basic algorithm was used for segmentation of the low contrast area of the wound area from the background in each image. Moreover, contrast limited adaptive histogram equalisation and edge detection techniques were applied in order to improve the quality of images. Program codes were generated and conformed to be the finalised program, named the wound healing measurement program for the trial version.

Results: The program was compared to ImageJ, with the results showing that there were no statistically significant differences (p>0.05, n=3). However, time for all processes of this program was fast and not dependent on the shape of the wound. Intra observer and inter-observer reproducibility of this study found a correlation coefficient within groups close to 1. The average satisfaction of observers for using the program was 4.45.

Conclusion: Thus, it was concluded that this study could apply the region growing technique for the measurement of cell migration program. However, there were problems that occurred during the proceeding in this version. Therefore, we aim to resolve and improve the efficiency of this program in future work.


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Phadngam, S., Ruthiang, J., Ina, N., Ferraresi, A., & Isidoro, C. (2020). Application program for wound healing measurement based on the region growing technique: A trial version. Journal of Associated Medical Sciences, 53(2), 1-6. Retrieved from
Radiologic Technology


[1] Morani F, Phadngam S, Follo C, Titone R, Aimaretti G, Galetto A, et al. PTEN regulates plasma membrane expression of glucose transporter 1 and glucose uptake in thyroid cancer cells. J Mol Endocrinol 2014; 53(2): 247-58.

[2] Morani F, Phadngam S, Follo C, Titone R, Thongrakard V, Galetto A, et al. PTEN deficiency and mutant p53 confer glucose-addiction to thyroid cancer cells: impact of glucose depletion on cell proliferation, cell survival, autophagy and cell migration. Genes & cancer 2014; 5(7-8): 226-39.

[3] Ferraresi A, Phadngam S, Morani F, Galetto A, Alabiso O, Chiorino G, et al. Resveratrol inhibits IL-6-induced ovarian cancer cell migration through epigenetic up-regulation of autophagy. Mol Carcinog 2017; 56(3): 1164-81.

[4] Mayrovitz HN, Soontupe LB. Wound areas by computerized planimetry of digital images: accuracy and reliability. Adv Skin Wound Care 2009; 22(5): 222-9.

[5] Nunes JPS, Dias AAM. ImageJ macros for the user-friendly analysis of soft-agar and wound-healing assays. Biotechniques 2017; 62(4): 175-9.

[6] Aragon-Sanchez J, Quintana-Marrero Y, Aragon-Hernandez C, Hernandez-Herero MJ. ImageJ: A Free, Easy, and Reliable Method to Measure Leg Ulcers Using Digital Pictures. Int J Low Extrem Wounds 2017; 16(4): 269-73.

[7] Yian LC, Xiaobo L. Adaptive image region-growing. IEEE Trans Image Process 1994; 3(6): 868-72.

[8] Zhang X, Xiongfei L, Feng Y. A medical image segmentation algorithm based on bi-directional region growing. Opt - Int J Light Electron Opt 2015; 126(20): 2398–404.

[9] Ozturk CN, Albayrak S. Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling. Comput Biol Med 2016; 72: 90–107.

[10] Yousefi S, Qin J, Zhi Z, Wang RK. Uniform enhancement of optical micro-angiography images using Rayleigh contrast-limited adaptive histogram equalization. Quant Imaging Med Surg 2013; 3(1): 5-17.

[11] Flores WG, Pereira WC. A contrast enhancement method for improving the segmentation of breast lesions on ultrasonography. Comput Biol Med 2017; 80: 14-23.

[12] Canny JF. A computational approach to edge detection. IEEE Trans Pattern Anal Machine Intell 1986; 8: 679-714.

[13] Davis LS. Survey of edge detection techniques. Comput Graph Image Process 1975; 4: 248–270.

[14] Qian RJ, Huang TS. Optimal edge detection in two-dimensional images. IEEE Trans Image Process 1996; 5: 1215–20.