To detect the kidney stones in ultrasound images using median filters to improve the detection rate in terms of accuracy and sensitivity. Materials and Methods: The accuracy and sensitivity of median filter (n=114) was compared with rank filter (n=114). The median filter is used to detect the kidney stone in ultrasound images. 114 is the sample size taken with the p-value 0.8 and has been used to improve detection rate of kidney stones in terms of accuracy and sensitivity using Matlab simulation tool.
Study setting of proposed work is done in our university. The number of groups identified for the study is 2. The group 1 is median filter and group 2 is rank filter. Matlab 2014a tool kit will be used to write the code and simulate. Using matlab accuracy and sensitivity has been calculated for the required algorithm and then results have been compared. Sample size per group is 114 (Kane, Phar, and BCPS n.d.). Median filter and rank filter are explained below. SPSS software has been used to compare the results and to find the graph. The pre-test analysis has done with p-value with 0.8 (gpower 80%).
Median filter and Rank filter algorithm
Accuracy and sensitivity of the rank filter are analyzed by varying different ultrasound images
in the MATLAB simulation tool. Matlab (2014a) will be used for simulation with required add-ons
installed, these are predefined functions in the matlab for the image processing. Open matlab software
and open new m.file. Write the code for the rank filter and save the file in the desired location. Store
the input images in the location using the rank filter algorithm. Then extract kidney images and find
the stone in the ultrasound image. After processing the code the output image will be displayed in the
command window and repeat the experiment for different kidney ultrasound images and get the
output and find the detection rate using the formula. Kidney stone ultrasound images are taken as
input images which are independent variables. Accuracy and sensitivity will be as output variables.
By comparing the results a better algorithm has been decided. Detection rate of the algorithms will be
calculated using the formula.
Detection rate = (No. of output images/Total input images)*100
Kidney stone detection using median filter in Matlab simulation tool and the output obtained for stone detection With the help of present algorithms doctors can look forward to appropriate treatment methods which can result in the removal of stone from kidneys in an appropriate manner. the accuracy and sensitivity for different samples for Median filter and Rank filter algorithm. These results were obtained by simulating the images in Matlab. In this 18 results for sample images has been taken and were shown in the table. This can be useful in comparing the both algorithms.
Based on the results and tabulations, the detection rate of the kidney stones in ultrasound images using median filters is improved in terms of accuracy (86.4%) and sensitivity (87.7%) compared with the accuracy (82.2%) and Sensitivity (82.5%) of rank filter.
MATLAB MAIN CODE:
clc
clear all
close all
warning off
[filename, pathname]=uigetfile('*.*', 'Pick a MATLAB code file');
filename=strcat(pathname,filename);
a=imread(filename);
imshow(a);
b=rgb2gray(a);
figure;
imshow(b);
impixelinfo;
c=b>20;
figure;
imshow(c);
d=imfill(c,'holes');
figure;
imshow(d);
e=bwareaopen(d,1000);
figure;
imshow(e);
PreprocessedImage=uint8(double(a).*repmat(e,[1 1 3]));
figure;
imshow(PreprocessedImage);
PreprocessedImage=imadjust(PreprocessedImage,[0.3 0.7],[])+50;
figure;
imshow(PreprocessedImage);
uo=rgb2gray(PreprocessedImage);
figure;
imshow(uo);
mo=medfilt2(uo,[5 5]);
figure;
imshow(mo);
po=mo>250;
figure;
imshow(po);
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