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Analysis of Kidney Ultrasound Images Using Deep Learning and Machine Learning Techniques: A Review

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Pervasive Computing and Social Networking

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 317))

Abstract

Ultrasonography is the most accepted and widely used imaging technique due to its non-invasive and radiation-free nature. The heterogeneous structure of kidney makes the disease detection a difficult task. Hence, more efficient models and methods are required to assist radiologists in making precise decisions. Since ultrasound imaging is considered to be the initial step in the diagnosis, more efficient processing techniques are needed in the interpretation of images. The presence of speckle noise is a challenge task in image processing. It diminishes the clarity of the images. In this article, an in-depth review has been performed on various machine learning and deep learning techniques, which are helping to improve the quality of images. The pre-processing, segmentation, feature extraction, and classification are described in detail using kidney cyst, stone, tumor, and normal kidney images. Deep learning techniques are enhancing the quality of the images with better accuracy. The remaining challenges and directions for future research are also explored.

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References

  1. Correas J-M, Anglicheau D, Joly D, Gennisson J-L, Tanter M, Hélénon O (2016) Ultrasound-based imaging methods of the kidney—recent developments. Kidney Int 90(6):1199–1210

    Article  Google Scholar 

  2. Kahn PC (1979) Renal imaging with radionuclides, ultrasound, and computed tomography. In: Seminars in nuclear medicine, vol 9, no 1. WB Saunders, pp 43–57

    Google Scholar 

  3. Goodman JW (1977) Some fundamental properties of speckle. JOSA 66(11):1145–1150

    Article  Google Scholar 

  4. Gupta S, Chauhan RC, Sexana C (2004) Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Med Bio Eng Comput 42(2):189–192

    Article  Google Scholar 

  5. Nicolae CM, Moraru L (2011) Image analysis of kidney using wavelet transform. Annals Univer Craiova-Math Comput Sci Series 38(1):27–34

    MathSciNet  MATH  Google Scholar 

  6. Khare A, Khare M, Jeong Y, Kim H, Jeon M (2010) Despeckling of medical ultrasound images using Daubechies complex wavelet transform. Signal Proc 90(2):428–439

    Article  MATH  Google Scholar 

  7. Adamo F, Andria G, Attivissimo F, Lanzolla AML, Spadavecchia M (2013) A comparative study on mother wavelet selection in ultrasound image denoising. Measurement 46(8):2447–2456

    Article  Google Scholar 

  8. Leal AS, Paiva HM (2019) A new wavelet family for speckle noise reduction in medical ultrasound images. Measurement 140:572–581

    Article  Google Scholar 

  9. Achim A, Bezerianos A, Tsakalides P (2001) Novel Bayesian multiscalemethod for speckle removal in medical ultrasound images. IEEE Trans Med Imaging 20(8):772–783

    Article  Google Scholar 

  10. Gupta S, Kaur L, Chauhan RC, Saxena SC (2007) A versatile technique for visual enhancement of medical ultrasound images. Digital Signal Proc 17(3):542–560

    Article  Google Scholar 

  11. Arora H, Mittal N (2019) Image enhancement techniques for gastric diseases detection using ultrasound images. In: 2019 3rd international conference on electronics, communication and aerospace technology (ICECA). IEEE, pp 251–256

    Google Scholar 

  12. Georgieva V, Petrov P, Mihaylova A (2018) Ultrasound image processing for improving diagnose of renal diseases. In: 2018 IX national conference with international participation (ELECTRONICA). IEEE, pp 1–4

    Google Scholar 

  13. Supriyanto E, Tahir NA, Nooh SM, Arooj A, Hafizah W (2011) Automatic ultrasound kidney’s centroid detection system. In: Proceedings of the 15th WSEAS international conference on Computers, World Scientific and Engineering Academy and Society (WSEAS), pp 160–165

    Google Scholar 

  14. Li L, Ross P, Kruusmaa M, Zheng X (2011) A comparative study of ultrasound image segmentation algorithms for segmenting kidney tumors. In: Proceedings of the 4th international symposium on applied sciences in biomedical and communication technologies, pp 1–5

    Google Scholar 

  15. Mendoza CS, Kang X, Safdar N, Myers E, Peters CA, Linguraru MG (2013) Kidney segmentation in ultrasound via genetic initialization and active shape models with rotation correction. In: 2013 IEEE 10th international symposium on biomedical imaging. IEEE, pp 69–72

    Google Scholar 

  16. Jokar E, Pourghassem H (2013) Kidney segmentation in Ultrasound images using curvelet transform and shape prior. In: 2013 international conference on communication systems and network technologies. IEEE, pp 180–185

    Google Scholar 

  17. Huang J, Yang X, Chen Y, Tang L (2013) Ultrasound kidney segmentation with a global prior shape. J Vis Commun Image Represent 24(7):937–943

    Article  Google Scholar 

  18. Rahman T, Uddin MS (2013) Speckle noise reduction and segmentation of kidney regions from ultrasound image. In: 2013 international conference on informatics, electronics and vision (ICIEV). IEEE, pp 1–5

    Google Scholar 

  19. Jeyakumar V, Kathirarasi Hasmi M (2013) Quantitative analysis of segmentation methods on ultrasound kidney image. Int J Adv Res Comput Commun Eng 2(5):1974–1978

    Google Scholar 

  20. Gupta A, Gosain B, Kaushal S (2013) A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen. J Clin Monit Comput 24(5):341–362

    Article  Google Scholar 

  21. Xie J, Jiang Y, Tsui H-T (2005) Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans Med Imaging 24(1):45–57

    Article  Google Scholar 

  22. Selvathi D, Bama S (2017) Phase based distance regularized level set for the segmentation of ultrasound kidney images. Pattern Recogn Lett 86:9–17

    Article  Google Scholar 

  23. Zheng Q, Warner S, Tasian G, Fan Y (2018) A dynamic graph cuts method with integrated multiple feature maps for segmenting kidneys in 2D ultrasound images. Acad Radiol 25(9):1136–1145

    Article  Google Scholar 

  24. Raja KB, Reddy MR, Swaranamani S, Suresh S, Madheswaran M, Thyagarajah K (2003) Study on ultrasound kidney images for implementing content-based image retrieval system using regional gray-level distribution. In: Proceeding international conference on advances in infrastructures for electronic business, education, science, medicine, and mobile technologies on the internet, pp 6–12

    Google Scholar 

  25. Manikandan S, Rajamani V (2008) A mathematical approach for feature selection and image retrieval of ultra sound kidney image databases. Eur J Sci Res 24(2):163–171

    Google Scholar 

  26. Christiyana CC, Rajamani V (2012) Comparison of local binary pattern variants for ultrasound kidney image retrieval. Int J Adv Res Comput Sci Software Eng 2(10):224–228

    Google Scholar 

  27. Manikandan S, Rajamani V (2010) Automated feature extraction and retrieval of ultrasound kidney images using maximin approach. Int J Comput Appl 4(1):42–46

    Google Scholar 

  28. Hafizah WM, Supriyanto E, Yunus J (2012) Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix. In: 2012 sixth Asia modelling symposium. IEEE, pp 115–120

    Google Scholar 

  29. Karthikeyini C, Bommanna Raja K, Madheswaran M (2004) Study on ultrasound kidney images using principal component analysis: a preliminary result. In: ICVGIP, pp 190–195

    Google Scholar 

  30. Ardon R, Cuingnet R, Bacchuwar K, Auvray V (2015) Fast kidney detection and segmentation with learned kernel convolution and model deformation in 3D ultrasound images. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI). IEEE, pp 268–271

    Google Scholar 

  31. Cerrolaza JJ, Safdar N, Biggs E, Jago J, Peters CA, Linguraru MG (2016) Renal segmentation from 3D ultrasound via fuzzy appearance models and patient-specific alpha shapes. IEEE Trans Med Imaging 35(11):2393–2402

    Article  Google Scholar 

  32. Marsousi M, Plataniotis KN, Stergiopoulos S (2016) An automated approach for kidney segmentation in three-dimensional ultrasound images. IEEE J Biomed Health Inf 21(4):1079–1094

    Article  Google Scholar 

  33. Raja KB, Madheswaran M, Thyagarajah K (2007) Ultrasound kidney image analysis for computerized disorder identification and classification using content descriptive power spectral features. J Med Syst 31(5):307–317

    Article  Google Scholar 

  34. Dhanalakshmi K, Rajamani V (2010) An efficient association rule-based method for diagnosing ultrasound kidney images. In: 2010 IEEE international conference on computational intelligence and computing research. IEEE, pp 1–5

    Google Scholar 

  35. Jose JS, Sivakami R, Uma Maheswari N, Venkatesh R (2012) An efficient diagnosis of kidney images using association rules. Int J Comput Technol Electron Eng 2(2):14–20

    Google Scholar 

  36. Subramanya MB, Kumar V, Mukherjee S, Saini M (2015) SVM-based CAC system for B-mode kidney ultrasound images. J Digital Imaging 28(4):448–458

    Article  Google Scholar 

  37. Sharma K, Virmani J (2017) A decision support system for classification of normal and medical renal disease using ultrasound images: a decision support system for medical renal diseases. Int J Ambient Comput Intell (IJACI) 8(2):52–69

    Article  Google Scholar 

  38. Nithya A, Appathurai A, Venkatadri N, Ramji DR, Anna Palagan C (2020) Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images. Measurement 149:106952

    Article  Google Scholar 

  39. Vasanthselva Kumar R, Balasubramanian M, Palanivel S (2017) Pattern analysis of kidney diseases for detection and classification using ultrasound b-mode images. Int J Pure Appl Math 117(15):635–653

    Google Scholar 

  40. Pujari RM, Hajare VD (2014) Analysis of ultrasound images for identification of chronic kidney disease stages. In: Proceeding of first international conference on networks & soft computing (ICNSC2014)’. IEEE, pp 380–383

    Google Scholar 

  41. Chen CJ, Pai TW, Fujita H, Lee CH, Chen YT, Chen KS, Chen YC (2014) Stage diagnosis for Chronic Kidney Disease based on ultrasonography, In: 2014 11th international conference on fuzzy systems and knowledge discovery (FSKD). IEEE, pp 525–530

    Google Scholar 

  42. Hsieh JW, Lee CH, Chen YC, Lee WS, Chiang HF (2014) Stage classification in chronic kidney disease by ultrasound image. In: Proceedings of the 29th international conference on image and vision computing New Zealand, pp 271–276

    Google Scholar 

  43. Iqbal F, Pallewatte AS, Wansapura JP (2017) Texture analysis of ultrasound images of chronic kidney disease. In: 2017 seventeenth international conference on advances in ICT for emerging regions (ICTer). IEEE, pp 1–5

    Google Scholar 

  44. Ardiatna W, Saputro AH, Soejoko DS (2018) Analysis of kidney ultrasound images characterization using statistical moment descriptor. In: 2018 international conference on computer, control, informatics and its applications (IC3INA). IEEE, pp 17–22

    Google Scholar 

  45. Shah SR, Desai MD, Panchal L (2010) Identification of content descriptive parameters for classification of renal calculi. Int J Signal Image Proc 1(4)

    Google Scholar 

  46. Tamilselvi PR, Thangaraj P (2011) Computer aided diagnosis system for stone detection and early detection of kidney stones. J Comput Sci 7(2):250

    Article  Google Scholar 

  47. Page A, Hassanalieragh M, Soyata T, Aktas MK, Kantarci B, Andreescu S (2017) Conceptualizing a real-time remote cardiac health monitoring system. In: Medical imaging: concepts, methodologies, tools, and applications. IGI Global, pp 160–193

    Google Scholar 

  48. Raja RA, Jennifer Ranjani J (2013) Segment based detection and quantification of kidney stones and its symmetric analysis using texture properties based on logical operators with ultrasound scanning. Int J Comput Appl 975:8887

    Google Scholar 

  49. Dahiya A, Dubey RB (2015) Survey of some multilevel thresholding techniques for medical imaging. Int J Sci, Res 3(7):103–106

    Google Scholar 

  50. Ranjitha M (2016) Extraction and dimensionality reduction of features for renal calculi detection and artifact differentiation from segmented ultrasound kidney images. In: 2016 3rd international conference on computing for sustainable global development (INDIACom). IEEE, pp 3087–3092

    Google Scholar 

  51. Monika P, Harsh S, Sukhdev S (2016) Features extraction and classification for detection of kidney stone region in ultrasound images. Int J Multi Res Dev 3(5):81–83

    Google Scholar 

  52. Verma J, Nath M, Tripathi P, Saini KK (2017) Analysis and identification of kidney stone using Kth nearest neighbour (KNN) and support vector machine (SVM) classification techniques. Pattern Recogn Image Anal 27(3):574–580

    Article  Google Scholar 

  53. Selvarani S, Rajendran P (2019) Detection of renal Calculi in ultrasound image using meta-heuristic support vector machine. J Med Syst 43(9):300

    Article  Google Scholar 

  54. Fenster A, Chiu B (2006) Evaluation of segmentation algorithms for medical imaging. In: 2005 IEEE engineering in medicine and biology 27th annual conference. IEEE, pp 7186–7189

    Google Scholar 

  55. Meiburger KM, Rajendra Acharya U, Molinari F (2018) Automated localization and segmentation techniques for B-mode ultrasound images: a review. Comput Bio Med 92:210–235

    Article  Google Scholar 

  56. Torres HR, Queiros S, Morais P, Oliveira B, Fonseca JC, Vilaca JL (2018) Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: a systematic review. Comput Methods Programs Biomed 157:49–67

    Article  Google Scholar 

  57. Zhou Z, Wang Y, Guo Y, Qi Y, Jinhua Y (2019) Image quality improvement of hand-held ultrasound devices with a two-stage generative adversarial network. IEEE Trans Biomed Eng 67(1):298–311

    Article  Google Scholar 

  58. Dokur Z, Ölmez T (2008) Segmentation of ultrasound images by using a hybrid neural network. Pattern Recogn Lett 23(14):1825–1836

    Article  MATH  Google Scholar 

  59. Ravishankar H, Venkataramani R, Thiruvenkadam S, Sudhakar P, Vaidya V (2017) Learning and incorporating shape models for semantic segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 203–211

    Google Scholar 

  60. Tabrizi PR, Mansoor A, Cerrolaza JJ, Jago J, George Linguraru M (2018) Automatic kidney segmentation in 3D pediatric ultrasound images using deep neural networks and weighted fuzzy active shape model. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, pp 1170–1173

    Google Scholar 

  61. Yin S, Zhang Z, Li H, Peng Q, You X, Furth SL, Tasian GE, Fan Y (2019) Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE, pp 1741–1744

    Google Scholar 

  62. Raja KB, Madheswaran M, Thyagarajah K (2007) Analysis of ultrasound kidney images using content descriptive multiple features for disorder identification and ANN based classification. In: 2007 international conference on computing: theory and applications (ICCTA'07). IEEE, pp 382–388

    Google Scholar 

  63. Chaitanya SMK, Rajesh Kumar P (2018) Oppositional gravitational search algorithm and artificial neural network-based classification of kidney images. J Intell Syst 29(1):485–496

    Article  Google Scholar 

  64. Chaitanya SMK, Rajesh Kumar P (2019) Classification of kidney images using cuckoo search algorithm and artificial neural network. Int J Eng Adv Technol 8(3):370–374

    Google Scholar 

  65. Raja KB, Madheswaran M, Thyagarajah K (2008) A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features. J Med Syst 32(1):65–83

    Article  Google Scholar 

  66. Attia MW, Abou-Chadi FEZ, Moustafa HED, Mekky N (2015) Classification of ultrasound kidney images using PCA and neural networks. Int J Adv Comput Sci Appl 6(4):53–57

    Google Scholar 

  67. Viswanath K, Gunasundari R (2014) Design and analysis performance of kidney stone detection from ultrasound image by level set segmentation and ANN classification. In: 2014 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 407–414

    Google Scholar 

  68. Mangayarkarasi T, Najumnissa Jamal D (2017) PNN-based analysis system to classify renal pathologies in kidney ultrasound images. In: 2017 2nd international conference on computing and communications technologies (ICCCT). IEEE, pp 123–126

    Google Scholar 

  69. Cheng PM, Malhi HS (2017) Transfer learning with convolutional neural networks for classification of abdominal ultrasound images. J Digit Imaging 30(2):234–243

    Article  Google Scholar 

  70. Zheng Q, Furth SL, Tasian GE, Fan Y (2019) Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. J Pediatr Urol 15(1):75-e1

    Article  Google Scholar 

  71. Kuo C-C, Chang C-M, Liu K-T, Lin W-K, Chiang H-Y, Chung C-W, Ho M-R, Sun P-R, Yang R-L, Chen K-T (2019) Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. NPJ Digital Med 2(1):1–9

    Article  Google Scholar 

  72. Krishna KD, Akkala V, Bharath R, Rajalakshmi P, Mohammed AM, Merchant SN, Desai UB (2016) Computer aided abnormality detection for kidney on FPGA based IoT enabled portable ultrasound imaging system. IRBM 37(4):189–197

    Article  Google Scholar 

  73. Akkasaligar PT, Biradar S (2016) Diagnosis of renal calculus disease in medical ultrasound images. In: 2016 IEEE international conference on computational intelligence and computing research (ICCIC). IEEE, pp 1–5

    Google Scholar 

  74. Viswanath K, Gunasundari R (2015) Analysis and implementation of kidney stone detection by reaction diffusion level set segmentation using xilinx system generator on FPGA. VLSI Design

    Google Scholar 

  75. Dhindsa K, Smail LC, McGrath M, Braga LH, Becker S, Sonnadara RR (2018) Grading prenatal hydronephrosis from ultrasound imaging using deep convolutional neural networks. In: 2018 15th conference on computer and robot vision (CRV). IEEE, pp 80–87

    Google Scholar 

  76. Wu Y, Yi Z (2020) Automated detection of kidney abnormalities using multi-feature fusion convolutional neural networks. Knowledge-Based Syst 105873

    Google Scholar 

  77. Zeng X, Wen L, Liu B, Qi X (2019) Deep learning for ultrasound image caption generation based on object detection. Neurocomputing 392:132–141

    Article  Google Scholar 

  78. Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX, Ni D, Wang T, Deep learning in medical ltrasound analysis: a review. Engineering 5(2):261–275

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, CHRIST (Deemed To Be University), Bengaluru, India

    Mino George & H. B. Anita

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  1. Mino George
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  2. H. B. Anita
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Correspondence to Mino George .

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Editors and Affiliations

  1. Electronics And Communication Engineering, Gnanamani College of Technology, Namakkal, India

    G. Ranganathan

  2. Czech Technical University in Prague, Prague, Czech Republic

    Robert Bestak

  3. Department at the Gerald Schwartz School of Business, St. Francis Xavier University,, Nova Scotia, NS, Canada

    Ram Palanisamy

  4. Department of Informatics Engineering, AISTI & University of Coimbra, Coimbra, Portugal

    Álvaro Rocha

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George, M., Anita, H.B. (2022). Analysis of Kidney Ultrasound Images Using Deep Learning and Machine Learning Techniques: A Review. In: Ranganathan, G., Bestak, R., Palanisamy, R., Rocha, Á. (eds) Pervasive Computing and Social Networking. Lecture Notes in Networks and Systems, vol 317. Springer, Singapore. https://doi.org/10.1007/978-981-16-5640-8_15

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