Due to the great growth in the usage of computer technologies, image-processing techniques have become one among the most significant as well as rapidly used one in a broad variety of applications, particularly in medical imaging. Medical images are mostly used as radiographic techniques in disease recognition, clinical examinations along with treatment planning. The basic idea of medical image analysis is to develop imaging content. A typical medical imaging system is composed of five major processing phases i.e., image acquisition, pre-processing, segmentation, feature extraction/selection, and classification. Medical scan image usage machines are also constantly as fundamental. With these devices, it is possible to quicken and advance the errand of the examination of the diseases. Here, we have done a study on the present advanced techniques that have been used in various stages of medical image processing along with various medical image tools will be analyzed in a few directions. The essential focus of the assessment is to aggregate and examination on the medical apparatus in order to propose clients of different working systems on what sort of medical image devices to be utilized while investigating different kinds of imaging.

Medical Imaging Tools, Pre-Processing, Segmentation, Feature Extraction, Classification

[1]. Bo Zhang, Computer Vision vs. Human Vision
[2]. R. Procter, “Definition of Health Informatics”. Internet: https://www.nlm.nih.gov/hsrinfo/informatics.html, Aug. 01, 2016, [Aug. 03, 2016].
[3]. Pettit, C., et al. Developing a multi-scale visualization toolkit for use in climate change response. Landscape Ecol (2012).
[4]. MarjanLaal a "Innovation Process in Medical Imaging” Procedia - Social and Behavioral Sciences 81 (2013) 60 – 64, ELSEVIER.
[5]. Nolden, Marco, et al. The medical imaging interaction toolkit: challenges and advances. International journal of computer assisted radiology and surgery 8.4 (2013): 607-620.
[6]. https://slideplayer.com/slide/3898447/
[7]. HassenLazrag, Med Saber Naceur, “Despeckling of Intravascular Ultrasound Images using Curvelet Transform”. (IEEE- SETIT 2012)
[8]. https://www.heart.org/en/health-topics/heart-attack/diagnosing-a-heart-attack/single-photon-emission-computed-tomography-spect
[9]. Matthias Becker and Nadia Magnenat - Thalmann “Muscle Tissue Labeling of Human Lower Limb in Multi - Channel mDixon MR Imaging: Concepts and Applications”. IEEE / ACM Transactions on Computational Biology and Bioinformatics.
[10]. https://www.frontiersin.org/articles/10.3389/frobt.2018.00120/full
[11]. Solomon, Chris, and Toby Breckon. Fundamentals of Digital Image Processing: A practical approach with examples in Matlab. John Wiley & Sons, 2011.
[12]. Lee, Lay Khoon, Siau Chuin Liew, and Weng Jie Thong. A Review of Image Segmentation Methodologies in Medical Image. Advanced Computer and Communication Engineering Technology. Springer International Publishing, 2015. 1069-1080.
[13]. Cook, P. A. et al. Camino: open-source diffusion-MRI reconstruction and processing. In 14th scientific meeting of the international society for magnetic resonance in medicine 2006, May (Vol. 2759).
[14]. K.M.M. Rao, V.D.P. Rao, Medical Image Processing.
[15]. Solomon, Chris, and Toby Breckon. Fundamentals of Digital Image Processing: A practical approach with examples in Matlab. John Wiley & Sons, 2011.
[16]. Gonzalez Rafael C. Digital Image Processing, Pearson Education India, 2009
[17]. Daizy Deb, Bahnishikha Dutta, Sudipta Roy, A Noble Approach for Noise Removal from Brain Image using Region Filling (ICACCCT-2014)
[18]. A. P. Dhawan, “Image Segmentation and Feature Extraction”, in Principles And Advanced Methods in Medical Imaging And Image Analysis. Singapore: World Scientific Publishing Co. Pte. Ltd, 2008, pp 197-228.
[19]. Semih Ergin, Onur Kilinc, “A new feature extraction framework based on wavelets for breast cancer diagnosis”, Computers in Biology and Medicine 51 (2014) 171–182 Elsevier.
[20]. A. Andrade, J. S. Silva, J. Santos, and P. Belo-soares, “Classifier Approaches for Liver Steatosis using Ultrasound Images,” in Procedia Technology HCIST 2012, pp. 763–770.
[21]. Abhinav Gupta, Bhuvan Gosain and Sunanda Kaushal, A Comparison of Two Algorithms for Automated Stone Detection in Clinical B-Mode ultrasound images of the abdomen. Journal of Clinical Monitoring and Computing (Springer-2010).
[22]. R. Vanithamani, G. Umamaheswari, “Wavelet based De-speckling of Medical Ultrasound Images with Bilateral filter”, TENCON IEEE-2011
[23]. V. P Gladis Pushpa Rathi and Dr. S. Palani, A Novel approach for Feature Extraction and selection on MRI images for brain tumor classification, S Computer Science & Information Technology (CS & IT) 2012.
[24]. R. Tomari, W. Nurshazwani, W. Zakaria, M. Mahadi, A. Jamil, F. Mohd, N. Farhan, and N. Fuad, “Computer Aided System for Red Blood Cell Classification in Blood Smear Image" in Procedia Computer Science ConfPRIDE 2013-2014, pp. 206–213.
[25]. Nailah Afshan, Shaima Qureshi, Syed Mujtiba Hussain Comparative Study of Tumor Detection Algorithms, (Medcom-2014).
[26]. Deepthi Murthy T.S. & G.Sadashivappa. Brain Tumor Segmentation Using Thresholding, Morphological Operations and Extraction of Features of Tumor, (ICAECC-2014)
[27]. Vaishali Kumbhakarna, Vijaya R. Patil, Dr. Seema Kawathekar “Review on Speckle Noise Reduction Techniques for Medical Ultrasound Image Processing”. Int J of Computer Techniques.
[28]. Y. Song, W. Cai, H. Huang, Y. Zhou, Y. Wang, and D. D. Feng, “Locality-constrained Subcluster Representation Ensemble for Lung Image Classification,” Medical Image Analysis, vol. 22, no. 1, pp. 102- 113, 2015.
[29]. Y. Iwahori, A. Hattori, Y. Adachi, M. K. Bhuyan, and J. Robert, “Automatic Detection of Polyp Using Hessian Filter and HOG Features,” in Procedia Computer Science International Conference in Knowledge Based and Intelligent Information and Engineering Systems - KES2015, pp. 730–739, 2015.
[30]. F. Yousefi Rizi, H. Ahmadi Noubari, S. K. Setarehdan Wavelet Based Ultrasound image denoising -Performance Analysis Comparison (EMBS 2011).
[31]. Yu‑Dong Zhang, Shuihua Wang, Xiao‑Jun Yang, Zheng‑Chao Dong, Ge Liu, Preetha Phillips, Ti‑Fei Yuan. “Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine”. Springerplus-2015.
[32]. K. Viswanath, R. Gunasundari, Syed Aathif Hussan, “VLSI Implementation and Analysis of Kidney Stone Detection by Level Set Segmentation and ANN Classification”. ICCC-2015 Elsevier
[33]. A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. Van Riel, M. W. Wille, M. Naqibullah, I. S. Clara, and B. Van Ginneken, “Pulmonary nodule detection in CT images : false positive reduction using multi-view convolutional networks,” IEEE Transactions on Medical Imaging, vol. 35, issue 5, 1160 - 1169, 2016.