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Top1. Introduction
There are several tests which can currently be used to diagnose kidney problem and diseases. Blood and urine tests can be performed to check for the kidney function (Tadashi Araki et al., 2015; Nobutaka Ikeda., 2015). In order to diagnose and finalize any disorders related to kidney blood vessels, patients can undergo kidney biopsy procedure. Imaging tests including intravenous pyelogram (IVP), ultrasound (Jadranka et.al and Alenka et.al, 2003), computed tomography (CT) (Alev et.al, 2010) and magnetic resonance imaging (MRI) scans. The albuminuria is also used as an early marker of kidney injury as it usually leads to the decline of renal function (Demetrius et.al, and Kelly A. Healy, 2012). Nowadays, availability of renal time US and automated biopsygun have improved the biopsy procedure to become more effective and safer through complication of bleeding still remains as one major area of uncertainty in kidney biopsy (Ngo, L. Y., Meng, O. L., Leong, G. B., and Guat, L. D., 2011). CT scan is an imaging technique that combines x-ray and computer technology for the production of cross-sectional images CT scan technique has superior sensitivity and specificity over all other modalities (Alev et.al, 2010). CT scan can be used to detect kidney stones, blockage, cysts and solid masses with more than 99% but problem with CT scan is contrast agent (John, R., 2007). US are often used as the initial imaging techniques because it can be performed safely, do not require any contrast agent and US scan is non-invasive, widely available.
Tanzila Rahman, Mohammad Shorif Uddin proposed reduction of speckle noise and segmentation from US image is discussed. It not only detect kidney region, but also enhance image quality (Anzila Rahman, T., & Mohammad Shorif Uddin, 2013). The wan Mahani Hafizah proposed kidney US images were divided into four dissimilar categories: normal, bacterial infection, cystic disease, kidney stones, based on gray level co-occurrence matrix (GLCM). From these categories doctors identify that the kidney is normal or abnormal (Wan Mahani Hafizah, 2012). Gladis Pushpa had proposed Hierarchical Self Organizing Map (HSOM) for brain tumours using segmentation, wavelets packets, and the results were correct up to maximum 97% (Tadashi Araki, MD., & Nobutaka Ikeda, 2015). Norihiro Koizumi proposed high intensity focused ultrasound (HIFU) technique, used for destroying tumours and stones (Viswanath, K., & Gunasundari. R, 2014). Bommanna Raja proposed content descriptive multiple features for disorder identification and artificial neural network (ANN) for classification and the results says that the maximum efficiency is 90.47%, and accuracy 86.66% only (Nilanjan Dey, & Sourav Samanta, 2013). The MLP- BP ANN is found as better performance in terms of accuracy having 92%, speed is 0.44 sec and sensitivity (Stevenson, et.al, Weinter, et.al and Widow, et.al, 1990). The Non-invasive combination of renal using pulsed cavitation US therapy proposed shock wave lithotripsy (ESWL) has become a standard for the treatment of calculi located in the kidney and ureter (Joge Martinez carballido, 2010). Mohammad E. Abou EI-Ghar projected location of urinary stones with unenhanced computed tomography (CT) using half-radiation (low) dose compared with the standard dose and of the 50 patients, 35 patients had a single stone while the rest of them had multiple stones(Morse, P. M, & Feshbach, H, 1953). In order to solve the local minima and segmentation problem the thord Andersson, Gunnar Lathen proposed modified gradient search and level set segmentation (Tadashi Araki, Nobutaka Ikeda, 2015). For 3D detection of kidneys and their pathology in real time, the Emmanouil Skounakis proposed templates based technique with accuracy of 97.2% and abnormalities in kidneys at an accuracy of 96.1% (William G. Robertson, 2012).