Feature extraction techniques with MATLAB

Feature extraction, a method commonly used in computer vision, image processing and artificial intelligence projects, is the application of size reduction on raw data. [1]. As you know, machine learning has been experiencing dramatic developments recently. This has led to great interest in machine learning from industry, academia and popular culture 🏭👩‍🔬. With the introduction of machine learning and deep learning models in the field of Health in the world recently, intelligent systems can detect many diseases in advance or do not overlook details that an expert cannot see [2]. There are different regions that can detect the disease on MRI images that are common in medical treatments ☢️. In order to concentrate in these regions, feature selection and feature extraction are carried out and the results obtained are reflected to various algorithms and the machine is provided to detect diseases detected by human beings.

Detection of lesion sites in sample brain MR image by feature inference [3]

I’m going to continue to work on left hand wrist MRI images taken from individuals that I mentioned earlier in my article “preliminary stages in bone age determination with image processing”. Of course, the images used are entirely up to the person’s request, if you wish you can also make feature inference on another image data set ✔️. The important thing is that we can recognize the objects in the image and determine what feature it contains. Figure 2 ‘ to briefly explain the image preprocessing stages in the MATLAB environment show. During the pre-processing stages, certain filters were used to erase the trivial details found in the image, reduce the light factor, and clarify certain areas.

MATLAB pre-processing steps
Examining of Feature Extraction

📌 Feature extraction is the acquisition of details by reducing the size in order to positively affect performance in a project. Attribute inference (feature inference), used in machine learning, pattern recognition, and image processing, creates derived values (properties) using measured data given as input [3].

📌 A feature in machine learning is the individually measurable property of an observed data. Features are inputs fed into the machine learning model to make a prediction or classification [4].

Steps of Data Analysis

Feature Extraction Techniques

Feature extraction aims to reduce the number of features in the data set by creating new features from existing data and then discarding the original features [6]. In line with this information, the color channels of the images were first checked in MATLAB. Then, according to the specified color channel RGB and Gray-Level information by keeping color conversions and numerical values will be obtained from these results. These numerical values will then be used in machine learning and will first be manually checked whether they belong to the same classes.

🔎 RGB, HSV, LAB Color Spaces and Examining of  GLCM

🔗 RGB Color Space : RGB is the most widely used color space. In this color model, each color acts as the main spectral components of red, green and blue. The Cartesian Coordinate System is in the infrastructure of this model. The color subspace of interest is examined as this cube, which is frequently used in image processing [7].

📌 When working in the RGB color channel, let’s check that the image received with priority is suitable for the RGB color channel, and then let’s keep the Matrix values of the red, green and blue color channels as variables.

Parsing the image into R, G and B channels

Representation of Sample Red Channel Values

🔗 HSV Color Space : The name of the HSV space comes from the initials of the words hue, saturation and brightness. HSV color space defines color with the terms Hue, Saturation, and Value. Although a mixture of Colors is used in RGB, HSV uses color, saturation and brightness values. Saturation determines the vitality of the color, while brightness refers to the brightness of the color.  The HSI space separates the nephew component in a color image from the hue and saturation, which are color-bearing information [9].

Parsing the image to H, S and V properties

Example Representation of V Channel Values

🔗 CIE Color Space : The CIE 1931 color spaces were the first defined quantitative connections between the distribution of wavelengths in the electromagnetic visible spectrum and physiologically perceived colors in human color vision . The mathematical relationships that define these color spaces are essential tools for Color Management, which are important when dealing with recording devices such as color inks, illuminated displays, and digital cameras [11] . To parse the RGB image into CIELAB channels, the transformation must be performed with the command rgb2lab.

Parsing the image into channels C, I and E

Representation of Sample C Channel Values

🔎 GLCM (Gray-Level Co-Occurrence Matrix) : Several tissue features can be extracted with the grey level co-formation Matrix. The texture filter functions provide a statistical view of the texture based on the image histogram. These functions can provide useful information about the texture of an image, but cannot provide information about the shape, that is, the spatial relationships of pixels in an image [12].

Calculation of sample GLCM values [12]

Creation of The Gray Level Co-Formation Matrix In The Image

🔔 Creation of the feature vector: In machine learning, feature vectors are used to represent numerical or symbolic properties of an object, called properties, in a mathematical, easily analyzable way. It is important for many different areas of machine learning and pattern processing. Machine learning algorithms often require a numerical representation of objects so that algorithms can perform processing and statistical analysis. Feature vectors are the equivalent of vectors of explanatory variables used in statistical procedures such as linear regression [13].

The resulting feature vector is a vector with values in size 1×28.

Graphitization of The Feature Vector

In this way we have obtained the feature vector. Hope to see you in my next post 🙌🏻

REFERENCES

[1] Sadi Evren Seker, “Feature Extraction”, December 2008, http://bilgisayarkavramlari.sadievrenseker.com/2008/12/01/ozellik-cikarimi-feature-extraction/.

[2] M. Mert Tunalı, “Brain tumor detection via MRI images Part 1 (U-Net)” taken from Medium.

[3] Shahab Aslani, Michael Dayan, Vittorio Murino, Diego Sona, “Deep 2D Encoder-Decoder Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation in Brain MRI”, September 2018, Conference Paper, MICCAI2018 (BrainLes Workshop).


[4] MC.AI, The Computer Vision Pipeline, Part 4: Feature Extraction, October 2019, https://mc.ai/the-computer-vision-pipeline-part-4-feature-extraction/.

[5] Javier Gonzalez-Sanchez, Mustafa Baydoğan, Maria-Elena Chavez-Echeagaray, Winslow Burleson, Affect Measurement: A Roadmap Through Approaches, Technologies, and Data Analysis, December 2017.

[6] Pier Paolo Ippolito, “Feature Extraction Techniques”, Towards Data Science, https://towardsdatascience.com/feature-extraction-techniques-d619b56e31be.

[7] C. Gonzalez, Rafael, E. Woods, Richard, Digital Image Processing, Palme Publishing, (Ankara, 2014).

[8] Retrieved from https://favpng.com/png_view/light-rgb-color-space-rgb-color-model-light-png/BsYUHtec.

[9] Dr. Lecturer Member of Caner Ozcan, Karabuk University, CME429 Introduction to Image Processing, “Color Image Processing”.

[10] Retrieved from https://tr.pinterest.com/pin/391179917623338540/.

[11] From Wikipedia, The Free Encyclopedia, “CIE 1931 Color Space”, April 2020, https://en.wikipedia.org/wiki/CIE_1931_color_space.

[12] Matlab, Image Processing Toolbox User’s Guide, “Using a Gray-Level Co-Occurrence Matrix (GLCM)”, http://matlab.izmiran.ru/help/toolbox/images/enhanc15.html.

[13] Brilliant, “Feature Vector”,  https://brilliant.org/wiki/feature-vector/, April 2020.

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