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Automated Lung Cancer Detection in Medical Imaging Using Image Processing Matlab Project with Source Code ABSTRACT The most common cause of lung cancer is long‐term exposure to tobacco smoke, which causes 80‐90% of lung can. The code is well documented, and Matlab syntax makes the operations fairly plain. It will be much easier to learn about the details of the detector from this code, I think, than from the optimized OpenCV implementation, for example. HOG Tutorial. For a tutorial on the HOG descriptor, check out my HOG tutorial post. Key Source Files. Face detection is a very difficult technique for young students, so we collected some useful matlab source code, hope they can help. The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there.

Matlab implementation of the HOG person detector.

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Some things you should know going into this:

• The HOG detector is compute intense, and this is not a highly-optimized implementation.
• The primary value in this code, I think, is to use it to learn about the HOG detector.
  • The code is well documented, and Matlab syntax makes the operations fairly plain.
  • It will be much easier to learn about the details of the detector from this code, I think, than from the optimized OpenCV implementation, for example.

HOG Tutorial

For a tutorial on the HOG descriptor, check out my HOG tutorial post.

Key Source Files

• runSingleWindowExample.m - Calculates the HOG descriptor for a single image that has been cropped down to the detector size. Look at this for learning about the descriptor by itself (without all of the complexities added by actually searching a full image for persons). It leverages the following two functions, which, along with my tutorial, are a good way to learn how the descriptor works.
  • getHOGDescriptor.m - Calculates the HOG descriptor for a given detection window.
  • getHistogram.m - Calculates the histogram for a single image cell.
• trainDetector.m - Trains a linear SVM on the ~2.2k pre-cropped windows in the /Images/Training/ folder. There is also already a pre-trained model saved in hog_model.mat, so you don't have to run this function in order to play with the examples.
• runSearchExample.m - Applies a pre-trained HOG detector to a sample validation image, reports the detector accuracy, and displays the image with true positives drawn.

The project also includes the following subdirectories:

• The search folder contains functions specifically related to searching an image for persons.
• The graphics folder just contains a function for resizing images, and another for plotting detection rectangles.
• The Images folder contains sample training and validation images.
• The svm folder contains everything needed to train a linear SVM.

Result Clustering

On the image search side, one of the most important things missing here is result clustering. I wrote a blog post on the OpenCV implementation of result clustering, but I haven't taken the time to port any of this over to Matlab yet.

Differences with OpenCV Implementation

The HOG descriptor implemented here is very similar to the original implementation and the one in OpenCV, but there are a few differences:

• OpenCV uses L2 hysteresis for the block normalization.
• OpenCV weights each pixel in a block with a Gaussian distribution before normalizing the block.
• The sequence of values produced by OpenCV does not match the order of the values produced by this code.

The image search functionality differs in many ways from the OpenCV implementation.

Order of Values

You may not need to understand the order of bytes in the final vector in order to work with it, but if you're curious, here's a description.

The values in the final vector are grouped according to their block. A block consists of 36 values: 1 block * 4 cells / block * 1 histogram / cell * 9 values / histogram = 36 values / block.

The first 36 values in the vector come from the block in the top left corner of the detection window, and the last 36 values in the vector come from the block in the bottom right.

Before unwinding the values to a vector, each block is represented as a 3D dimensional matrix, 2x2x9, corresponding to the four cells in a block with their histogram values in the third dimension. To unwind this matrix into a vector, I use the colon operator ':', e.g., A(:). You can reshape the values into a 3D matrix using the 'reshape' command. For example:

Hello friends, hope you all are fine and having fun with your lives. Today, I am going to share a new project which is Motion Detection in MATLAB. In this project, I am gonna detect the motion in MATLAB. This project was designed for security purposes and the condition was to use MATLAB instead of PIR Sensor. We all know that PIR sensor is used for motion detection but for that we have to design a hardware but using this software we can easily detect any motion using MATLAB.

In this project, I have used the webcam and then applied a simple image processing algorithm, designed in MATLAB. Using this algorithm I have detected the motion in the environment. That’s a quite simple project and you are gonna like this one. The code and complete simulation is given below for download. So, let’s get started with Motion Detection in MATLAB:

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Motion Detection in MATLAB

• First of all download the Motion Detection in MATLAB simulation by clicking the below button:

Download the MATLAB Simulation

• Now you can see it has three buttons on it.

• Now, click on the Start Comparison button and the software will start and will start capturing from the webcam.
• It will also show the captured image in the second zone and if there’s no change then the text below will show No Change, as shown in below figure:

• You can see as there’s no motion in the room that’s why it says No change in above figure.
• Now, let’s create some motion, so I am taking my hand in front of the cam and let’s see what results we got in the below figure:

MATLAB Codes

• The code behind Capture Image button is as follows:

• In the above code, first of all, I created an object of the webcam and then took a screenshot from that webcam.
• After that I saved that image in the project directory and named it as A.jpg.
• If you check your project directory then you will find this image.
• Now let’s have a look at the code behind Start Comparison Button:

• This is the main code of this Motion detection in MATLAB project.
• Here, again I am creating an object of the webcam and taking a screenshot.
• After that I am saving this screenshot in the project’s directory and I have renamed it as B.jpg.
• So, now I have two images A and B.
• After that I have converted both of these images to grey scale and calculated their histogram error and on the basis of this error I have detected the motion.
• Finally I have created a Serial Port object, if you don’t wanna use serial port then simply remove this code.

So, that’s all for today, I hope you have enjoyed the motion detection in MATLAB. If you have any questions, then ask in comments and I will resolve them.

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Category: MATLABBy Syed Zain Nasir7 Comments

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