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Medical image segmentation using automated rough density approach

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Abstract

Delineation of Gallbladder (GB) and identification of gallstones from Computed Tomography (CT) and Ultrasonography (USG) images is an essential step in the radiomic analysis of Gallbladder Cancer (GBC). In this study, we devise a method for effective segmentation of GB from 2D CT images and Gallstones from USG images, by introducing a Rough Density based Segmentation (RDS) method. Based on the threshold value obtained using rough entropy thresholding, the image is thresholded and passed as an input to the RDS method to obtain the desired segmented regions. To evaluate the performance of RDS method, we collected images from 30 patients exhibiting normal GB and 8 patients with gallstones. Additionally, the versatility of our RDS method has also been tested for segmenting lungs from a publicly available Covid-19 lung CT image dataset with cohort size of 20 patients. Our method has been compared with several well-known methods like hybrid fuzzy clustering, morphological active contour without edges, modified fuzzy c means and morphological geodesic active contours and found to give significantly better results with reference to Jaccard coefficient, Dice coefficient, accuracy, precision, sensitivity, specificity and McNemar’s test.

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Data Availability

The data that supports this study are available from the corresponding author on request.

References

  1. Kapoor VK (2007) Advanced gallbladder cancer: Indian Middle Path. J Hepato-Biliary-Pancreat Surg 14(4):366–373

    Article  Google Scholar 

  2. Stinton LM, Shaffer EA (2012) Epidemiology of gallbladder disease: cholelithiasis and cancer. Gut and liver 6(2):172

    Article  Google Scholar 

  3. Bhattacharjee PK et al (2018) Management of gallbladder carcinoma. J Med Sci 38(5):197

    Article  Google Scholar 

  4. Maini D, Aggarwal AK (2018) Camera position estimation using 2D image dataset. Int J Innov Eng Technol 10:199–203

    Google Scholar 

  5. Chopra J, Kumar A, Aggarwal AK, et al (2016) Biometric system security issues and challenges. In: Second international conference on innovative trends in electronics engineering (ICITEE2)

  6. Arora K, Aggarwal AK (2018) Approaches for image database retrieval based on color, texture, and shape features. In: Handbook of research on advanced concepts in real-time image and video processing. IGI Global, pp 28–50. https://doi.org/10.4018/978-1-5225-2848-7.ch002

  7. Tan L, Jiang J (2019). Image processing basics. https://doi.org/10.1016/b978-0-12-815071-9.00013-0

    Article  Google Scholar 

  8. Zhang H, Zhu Y, Zheng H (2019) NAMF: a non-local adaptive mean filter for salt-and-pepper noise removal. arXiv:1910.07787

  9. Zhu J, Wen J, Zhang Y (2013) A new algorithm for SAR image despeckling using an enhanced Lee filter and median filter. In: 2013 6th International congress on image and signal processing (CISP), pp 224–228. https://doi.org/10.1109/CISP.2013.6743991

  10. Mohd Sagheer SV, George SN (2020) A review on medical image denoising algorithms. Biomedical Signal Processing and Control 61(102):036. https://doi.org/10.1016/j.bspc.2020.102036. https://www.sciencedirect.com/science/article/pii/S1746809420301920

  11. Thukral R, Arora AS, Kumar A, et al (2022) Denoising of thermal images using deep neural network. In: Mahapatra RP, Peddoju SK, Roy S, et al (eds) proceedings of international conference on recent trends in computing. Springer Nature Singapore, Singapore, pp 827–833

  12. Thukral R, Kumar A, Arora A, et al (2019) Effect of different thresholding techniques for denoising of EMG signals by using different wavelets. In: 2019 2nd International conference on intelligent communication and computational techniques (ICCT), pp 161–165. https://doi.org/10.1109/ICCT46177.2019.8969036

  13. Lei B, Fan J (2019) Image thresholding segmentation method based on minimum square rough entropy.Appl Soft Comput 84:105,687. https://doi.org/10.1016/j.asoc.2019.105687. http://www.sciencedirect.com/science/article/pii/S1568494619304685

  14. Srikanth R, Bikshalu K (2021) Multilevel thresholding image segmentation based on energy curve with harmony search algorithm. Ain Shams Engineering Journal 12(1):1–20. https://doi.org/10.1016/j.asej.2020.09.003. https://www.sciencedirect.com/science/article/pii/S2090447920301970

  15. Bandyopadhyay O, Biswas A, Chanda B, et al (2013) Bone contour tracing in digital X-ray images based on adaptive thresholding. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8251 LNCS:465–473. https://doi.org/10.1007/978-3-642-45062-4_64

  16. Zhou J, Huang W, Zhang J, et al (2010) Segmentation of gallbladder from CT images for a surgical training system. In: Proceedings - 2010 3rd international conference on biomedical engineering and informatics, BMEI 2010 2(Bmei):536–540. https://doi.org/10.1109/BMEI.2010.5639989

  17. Lian J, Ma Y, Ma Y et al (2017) Automatic gallbladder and gallstone regions segmentation in ultrasound image. Int J CARS 12(4):553–568. https://doi.org/10.1007/s11548-016-1515-z

    Article  Google Scholar 

  18. Hannah Inbarani H, Azar AT, Jothi G (2020) Leukemia image segmentation using a hybrid histogram-based soft covering rough K-means clustering algorithm. Electronics (Switzerland) 9(1):1–22. https://doi.org/10.3390/electronics9010188

    Article  Google Scholar 

  19. Xian M, Zhang Y, Cheng HD, et al (2018) A Benchmark for breast ultrasound image segmentation (BUSIS) pp 1–9. arXiv:1801.03182

  20. Yang Y, Feng C, Wang R (2019) An automatic image segmentation model integrating fuzzy clustering with level set method. In: Proceedings of the third international symposium on image computing and digital medicine. Association for Computing Machinery, New York, NY, USA, ISICDM 2019, pp 222-225. https://doi.org/10.1145/3364836.3364880

  21. Gu K, Zhang Y, Qiao J (2021) Ensemble meta-learning for few-shot soot density recognition. IEEE Transactions on Industrial Informatics 17(3):2261–2270. https://doi.org/10.1109/TII.2020.2991208

    Article  Google Scholar 

  22. Gu K, Xia Z, Qiao J et al (2020) Deep dual-channel neural network for image-based smoke detection. IEEE Transactions on Multimedia 22(2):311–323. https://doi.org/10.1109/TMM.2019.2929009

    Article  Google Scholar 

  23. Gu K, Liu H, Xia Z, et al (2021) PM2.5 monitoring: use information abundance measurement and wide and deep learning. IEEE Transactions on Neural Networks and Learning Systems 32(10):4278–4290. https://doi.org/10.1109/TNNLS.2021.3105394

  24. Prasad RK, Sarmah R, Chakraborty S et al (2023) NNVDC: A new versatile density-based clustering method using k-Nearest Neighbors. Expert Syst Appl 227(120):250

    Google Scholar 

  25. Ester M, Kriegel HP, Sander J, et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: kdd, pp 226–231

  26. Zhang W, Zhang X, Zhao J, et al (2017) A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise. PloS one 12(9):e0184,290

  27. Abd Elaziz M, AA Al-Qaness M, Abo Zaid EO, et al (2021) Automatic clustering method to segment COVID-19 CT images. PLoS One 16(1):e0244,416

  28. Nixon MS, Aguado AS (2020) Image processing 3.1. https://doi.org/10.1016/B978-0-12-814976-8.00003-8

  29. Yushkevich PA, Piven J, Cody Hazlett H et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128

    Article  Google Scholar 

  30. Jitani N, Singha B, Barman G, et al (2022) Gallbladder CT image segmentation by integrating rough entropy thresholding with contours. In: Advanced computational paradigms and hybrid intelligent computing. Springer, pp 651–659

  31. Pal SK, Uma Shankar B, Mitra P (2005) Granular computing, rough entropy and object extraction. Pattern Recogn Lett 26(16):2509–2517. https://doi.org/10.1016/j.patrec.2005.05.007

    Article  Google Scholar 

  32. Jaccard P (1912) The distribution of the flora in the alpine zone. New Phytol 11(2):37–50

    Article  Google Scholar 

  33. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

    Article  Google Scholar 

  34. Baratloo A, Hosseini M, Negida A, et al (2015) Part 1: simple definition and calculation of accuracy, sensitivity and specificity

  35. Powers DM (2020) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv:2010.16061

  36. Gao Y, Mas JF, Kerle N et al (2011) Optimal region growing segmentation and its effect on classification accuracy. Int J Remote Sens 32(13):3747–3763. https://doi.org/10.1080/01431161003777189

    Article  Google Scholar 

  37. Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15(1):1–28

  38. Huang YP, Singh P, Kuo HC (2020) A hybrid fuzzy clustering approach for the recognition and visualization of mri images of parkinson’s disease. IEEE Access 8:25,041–25,051. https://doi.org/10.1109/ACCESS.2020.2969806

  39. Ashfaq A, Adler J (2017) A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images. In: CMBEBIH 2017. Springer, pp 531–538

  40. Marquez-Neila P, Baumela L, Alvarez L (2014) A morphological approach to curvature-based evolution of curves and surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(1):2–17. https://doi.org/10.1109/TPAMI.2013.106

    Article  Google Scholar 

  41. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277. https://doi.org/10.1109/83.902291

    Article  Google Scholar 

  42. Zimmer C (2021) The Secret Life of a Coronavirus–An oily, 100-nanometer-wide bubble of genes has killed more than two million people and reshaped the world. Scientists don’t quite know what to make of it

  43. Paiva O (2020) CORONACASES.ORG - helping radiologists to help people in more than 100 countries, coronavirus cases. http://www.kaggle.com/datasets/andrewmvd/covid19-ct-scans

  44. Glick Y (2020) Viewing Playlist: COVID-19 Pneumonia, Radiopaedia.Org. http://www.kaggle.com/datasets/andrewmvd/covid19-ct-scans

  45. Jun M, Cheng G, Yixin W, et al (2020) COVID-19 CT lung and infection segmentation dataset. https://doi.org/10.5281/zenodo.3757476

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Acknowledgements

The authors would like to thank Dr. Geetanjali Barman and R. Mala for their support in data collection and manual segmentation at Dr. B. Borooah Cancer Institute, Guwahati.

Funding

This work is an output of the research project titled “Radiomics with Machine Learning methods towards Prediction of Gallbladder Cancer” funded by ICMR.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Tezpur University, Tezpur, Assam, India

    Nitya Jitani, Bhaskar Jyoti Singha, Rosy Sarmah & Dhruba Kumar Bhattacharyya

  2. Department of Radiology, Dr. B. Borooah Cancer Institute, Guwahati, Assam, India

    Geetanjali Barman

  3. Department of Surgical Oncology, Dr. B. Borooah Cancer Institute, Guwahati, Assam, India

    Abhijit Talukdar

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  1. Nitya Jitani
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  2. Bhaskar Jyoti Singha
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Correspondence to Rosy Sarmah.

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Jitani, N., Singha, B.J., Barman, G. et al. Medical image segmentation using automated rough density approach. Multimed Tools Appl 83, 39677–39705 (2024). https://doi.org/10.1007/s11042-023-16921-6

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  • DOI: https://doi.org/10.1007/s11042-023-16921-6

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