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Survey of denoising, segmentation and classification of magnetic resonance imaging for prostate cancer

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Abstract

Prostate cancer (PCa) has become the second most dreadful cancer in men after lung cancer. Traditional approaches used for treatment of PCa were manual, time consuming and prone to subjective errors. Thus, there is a need for a Computer aided diagnosis system (CADs) consisting of denoising, segmentation, and classification approaches for diagnosis of PCa. CADs may act as a second opinion for the medical experts and save their precious time used in manual analysis. Magnetic resonance imaging (MRI) is the commonly used modality, as it produces detailed and fine contrast images of internal organs for diagnosis of PCa, but it may contain a certain amount of rician and gaussian noise which is necessary to be denoising before segmentation and classification. Denoising offers several challenges such as suppressing of significance image details leading in inaccurate segmentation and classification for prediction of abnormality. Thus, improved denoising, segmentation, and classification approaches can overcome the challenges by analyzing the pitfalls in the state of the art. This paper presents the experimental analysis state of the art denoising and segmentation approaches to analyse their performance based on the values of Peak signal to noise ratio (PSNR), Mean squared error (MSE), Structured similarity index (SSIM), dice metric, area overlap and accuracy. Based on the experimental analysis it was analysed that anisotropic filter outperforms other filters for gaussian noise with PSNR of 28.29, MSE of 96.22 and SSIM of 0.64. Also, for the rician noise anisotropic filter outperforms others with PSNR of 28.06, MSE of 101.52 and SSIM of 0.01. Similarly, for the combined gaussian and rician noise, anisotropic filter outperform others with PSNR of 28.34, MSE of 95.13 and SSIM of 0.652. Further, the analysis of segmentation approaches such as contour and shape-based, region/atlas based, thresholding based, clustering based and deep learning based was performed. Amongst these approaches deep learning based segmentation was found to outperform with dice metric of 0.89 and area overlap of 0.80. Also, CNN based classification outperformed machine learning based Support vector machine (SVM), K nearest neighbour (K-NN) and Random forest (RF) with 94.55% sensitivity, 93.34% specificity, 95.45% accuracy. Finally, the paper discusses challenges and future scope based on analysis in the concerned field for diagnosis of PCa.

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Notes

  1. https://i2cvb.github.io/#prostate-data

  2. https://wiki.cancerimagingarchive.net/display/Public/PROSTATE-MRI

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Acknowledgments

The authors are also grateful to the Ministry of Human Resource Development (MHRD), Govt. of India for funding this project(17-11/2015-PN-1) under Design Innovation Centre (DIC) sub-theme Medical Devices & Restorative Technologies.

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Authors and Affiliations

  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Mamta Juneja, Sumindar Kaur Saini, Jatin Gupta, Poojita Garg, Niharika Thakur, Aviral Sharma, Manan Mehta & Prashant Jindal

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  1. Mamta Juneja
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  2. Sumindar Kaur Saini
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Juneja, M., Saini, S.K., Gupta, J. et al. Survey of denoising, segmentation and classification of magnetic resonance imaging for prostate cancer. Multimed Tools Appl 80, 29199–29249 (2021). https://doi.org/10.1007/s11042-021-11044-2

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  • DOI: https://doi.org/10.1007/s11042-021-11044-2

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