Hello everyone, as I promised you before, I’m here to introduce FaCiPa. I will introduce you to the details of how to make an application from scratch by approaching the FaCiPa application step by step, which you have come across in many interviews, interviews, and practices. Excuse my excitement today, because I feel like every project I do is my child. So much so that this project is very valuable to me, as it also contains memories from my own life.
Category: Image processing
In the recent years, hyperspectral images obtained a major role as spectral signatures. Their continuous spectrum and multi-band structure allowed them to be powerful discriminators.
Feature extraction, a method commonly used in computer vision, image processing and artificial intelligence projects, is the application of size reduction on raw data. . 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 . 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.[gdlr_core_space height=”30px”] [gdlr_core_space height=”30px”]
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.[gdlr_core_space height=”30px”] [gdlr_core_space height=”30px”]
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 .
📌 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 .[gdlr_core_space height=”30px”]
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 . 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.[gdlr_core_space height=”30px”]
🔎 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 .
📌 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 .
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  . 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 .
Calculation of sample GLCM values 
🔔 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 .
The resulting feature vector is a vector with values in size 1×28.
In this way we have obtained the feature vector. Hope to see you in my next post 🙌🏻
 Sadi Evren Seker, “Feature Extraction”, December 2008, http://bilgisayarkavramlari.sadievrenseker.com/2008/12/01/ozellik-cikarimi-feature-extraction/.
 M. Mert Tunalı, “Brain tumor detection via MRI images Part 1 (U-Net)” taken from Medium.
 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).
 MC.AI, The Computer Vision Pipeline, Part 4: Feature Extraction, October 2019, https://mc.ai/the-computer-vision-pipeline-part-4-feature-extraction/.
 Javier Gonzalez-Sanchez, Mustafa Baydoğan, Maria-Elena Chavez-Echeagaray, Winslow Burleson, Affect Measurement: A Roadmap Through Approaches, Technologies, and Data Analysis, December 2017.
 Pier Paolo Ippolito, “Feature Extraction Techniques”, Towards Data Science, https://towardsdatascience.com/feature-extraction-techniques-d619b56e31be.
 C. Gonzalez, Rafael, E. Woods, Richard, Digital Image Processing, Palme Publishing, (Ankara, 2014).
 Dr. Lecturer Member of Caner Ozcan, Karabuk University, CME429 Introduction to Image Processing, “Color Image Processing”.
 Retrieved from https://tr.pinterest.com/pin/391179917623338540/.
 From Wikipedia, The Free Encyclopedia, “CIE 1931 Color Space”, April 2020, https://en.wikipedia.org/wiki/CIE_1931_color_space.
 Matlab, Image Processing Toolbox User’s Guide, “Using a Gray-Level Co-Occurrence Matrix (GLCM)”, http://matlab.izmiran.ru/help/toolbox/images/enhanc15.html.
 Brilliant, “Feature Vector”, https://brilliant.org/wiki/feature-vector/, April 2020.
Today, health studies in the field of artificial intelligence continue unabated. Now, the biggest assistants of health personnel in every field are artificial intelligence, algorithms, robotics working in health. In the age of growth, you have encountered to a child who has a height and growth disorder problem caused by genetic and growth disorders. In order to control short stature and growth impairment in these individuals, wrist x-ray film examinations are requested during the examination. So what do you think the doctors get from this X-ray film 👨⚕️? In these MRI images, skeletal maturation degree, called bone age, is calculated from the bones in the wrist. I can hear you say what the difference between bone and birth life calculated from the day of human birth can be 🗓️. The calculated age of an individual from the date of birth is the calendar age. Bone age is usually calculated from the left wrist X-ray, which occurs with the effect of hormones and nutrition 🦴
The major mismatch between skeletal (bone) age and chronological calendar age occurs in children with obesity or beginning early puberty. If you want to examine the carpal bones on the human wrist with you and find out what age bone is 🔍
What is Bone Age?
The degree of maturation of the bones is expressed as bone age. In a child with normal bone maturation, bone age should be equal to the chronological age. However, sometimes it is normal to see some deviation between bone age and chronological age.
🚩 Hand Wrist Carpal Bones
The wrist bones, which are referred to as Ossa Carpi in medical language, consist of 29 bones, including 2 forearm bones (radius and ulna), 8 wrist bones (carpals), 5 comb bones (metacarpals) and 14 finger bones. We will deal with carpal bones, which are used as wrist bones for bone age determination. Let’s get to know these bones better 🔍
📍 In Figure 2, The X-ray images of the carpal bones in the wrist, which are examined from an anatomical point of view, are given below.
📍 In children aged 0-6 years, the most important criterion when examining X-ray images is the number and size of secondary ossification centers and wrist bones in the epiphysis region.
📍 As shown in Figure 5, when the structural analysis of the long bone is performed, the epiphysis is considered to be one of the two ends of the long bone. As shown in Figure 5, it is the middle section of the long bone, and there is Metaphysis in the area between the epiphysis and the diaphyseal. Metaphysics is the area where the center of secondary ossification is located. The proportion of the areas to be taken from these regions and the age of the human can be determined. In this article I show you how the different filters that are the preliminary stages of image processing work. I chose Matlab as the programming platform. In MATLAB’S GUI platform, the image is easily selected and filters are applied.
🚩 Application Of Background Inference
Background extraction is a method often used in image processing applications to capture and track moving objects on a fixed ground. Removing unwanted radiological signs in the images used is possible with background removal method. Inference is performed in the background using the defined structural element.
Structural Element Selection
The background of the selected image is removed when the required operations are executed in the background.
🚩 Threshold Detection
It is a method used to convert an input image into a binary image. Binary image is the definition of the image in black and white. The goal is to identify the object by reducing the noise. As the first stage, MRI is performed on the image Threshold. In this way, the detection of the object can be easily detected in non-colored black-and-white images. OpenCV ‘ s threshold function cv2.THRESH_BINARY threshold type 127-255 with the view of the match is performed. değerlerinde eşikleme gerçekleştirilmektedir.
Original Image & Thresholded Image
🚩 Contrast Enhancement
Image enhancement techniques were examined one by one and the contrast enhancement method was chosen to provide the best results.
It is the process of expanding the niece level range to include the entire niece range of the recording medium or display device.
Contrast Enhancement Matlab Code
Matches the density values in the grayscale image with the new values given by the imadjust method.
Image intonation as a result of imadjust method
Contrast is enhanced by using the histogram equalization feature with the histeq method.
Histogram Equalization Matlab Code
Histogram Equalization Result Image Toning
Adaptive histogram equalization is used so that contrast is limited with the adapthisteq method.
Adaptive Histogram Equalization Matlab Code
Adaptive Histogram Equalization Result Image Toning
🚩 Applying Mean and Median Filters
The mean filter is the simplest filter that can be created with the help of a generated kernel. For each pixel, the surrounding pixels are averaged.
Mean Filter Import Matlab Code
Image Filtered Image Result
The median filter is assigned to the pixel whose median values are in the given neighborhood.
Median Filter Import Matlab Code
Image Result With Median Filter Applied
🚩 Applying of Laplace and Sobel filters
For image sharpening, the two-dimensional laplace operator is applied with a 0.2 percent filter.
Laplace Filter Import Matlab Code
Image Result Of Laplace Filter Application
In another of the image sharpening methods, more details are revealed on the image with the Sobel operator.
Sobel Filter Import Matlab Code
Sobel Filter Applied Image Result
🚩 Canny Edge Detection
The Canny edge detection algorithm is a multi-step algorithm that can detect edges with noise simultaneously being suppressed. It contains the steps to reduce noise with Gauss filter, gradient calculation with the use of gradient operators, detection of edges with Threshold values. For example, let’s run a Canny locator with threshold values (20, 70) on an MRI image with an individual’s hand and look at the results.
Canny Filter Import Matlab Code
Canny Filter Applied Image Result
🚩 Application of Erosion and Dilation Filters for Morphological Processes
The erosion filter erodes a grayscale or packaged binary image in accordance with the values of the given structural element. The dilation filter expands the worn areas of the worn image,making it easier to process. Let’s apply wear and expansion filters with the defined structural elements as follows.
Erosion and Dilation Filter Import Matlab Code
Erosion and dilation filters applied to the image result
The pre-processing stages of the image described above can be tested with different filters by changing the structural elements according to the projects to be applied. Thus, I have described the most commonly used pre-processing stages in the image. I wish codes on good days 💻
- Retrieved from http://www.cocukendokrindiyabet.org/haber/95.
- Gur Emre Guraksın, Selcuk University, Bone Age Detection Using Artificial Intelligence Techniques, Institute of Science, January 2015.
- Expert assistant Nurdan Akkan, Comparison of Greulich-Pyle and Tanner-Whitehouse Methods Used in Bone Age Detection , I.U. Faculty of Dentistry Dental and Jaw Orthopedics Unit, 1982.
- Esra HASALTIN, Erkan BESDOK, Use of Artificial Neural Networks in Radiological Bone Age Detection from Hand – Wrist X-Ray Images, Erciyes University, Computer Engineering Department, Faculty of Engineering, Institute of Science.
- Neyzi, O., Ertugrul, T., Pediatrics (2nd Edition), p. 61-100, Nobel Medical Bookstores, Istanbul, 1993.
- Retrieved from http://mesutpiskin.com/blog/opencv-arka-plan-cikarma-background-subtraction.html.
- Dr. Lecturer Member of Caner Ozcan, Karabük University, CME429 Introduction to Image Processing, Density Transforms and Histogram Processing.
- Retrieved from https://www.mathworks.com/help/images/ref/imadjust.html.
- Sıddık Acıl, Image Processing with Python: Taken from Medium, Mean and Median Filters.
- Retrieved from https://www.mathworks.com/help/images/ref/fspecial.html?searchHighlight=fspecial&s_tid=doc_srchtitle.
- Retrieved from https://www.mathworks.com/help/images/edge-detection.html?searchHighlight=canny.
- Retrieved from https://www.mathworks.com/help/images/ref/imerode.html?s_tid=doc_ta.
- Retrieved from https://www.mathworks.com/help/images/ref/imdilate.html?s_tid=doc_ta.
Welcome to the world of image processing 🎉I’m going to talk about color spaces in image processing, which is one of the areas of Computer Vision that are very important today. You know that image processing basically performs operations on the image. The use of color in image processing is due to two factors. First, color is an identifier that facilitates object recognition and object extraction from the image. Secondly, people can distinguish thousands of shades and intensity compared to gray shades only. For image analysis, we also need to specify the color channel to be used when performing various operations on the image. Before learning color channels, let’s learn a little about Color Image Processing. Scientifically, the foundations of the concept of color were discovered in 1665 by the British physicist Isaac Newton. In this experiment carried out in a dark room, it was noticed that the light coming through the door hole was shattered on the prism to form a color spectrum. Below is a summary drawing of this experiment.
We said the color spectrum but didn’ t mention what it means. The color spectrum is the separation of white light into its colors by passing through a special prism. In fact, we all know that very closely. To give a little more detail, rainbows that enthrall us with their colorful state after the rain are the most beautiful examples of color formation with the refraction of light 🎆.
Color spectrum formed when white light is passed through the prism 🌈
💎 The main reason for mentioning these is to present the spectrum range that the human eye can perceive in the most descriptive way. The spectrum range that the human eye can detect is 400 to 700 nm. This interval is defined as the scientifically visible region. This is exactly where the separation of white light passed through the prism you see in the photograph comes into play into the visible color spectrum. Here we’re going to work with this visible region. Let’s we talk about the most commonly used color channels !
As you know from everyday life and image processing, the main colors are Red, Green and Blue. The color channel consisting of these colors is called RGB in scientific terms. These primary colors come together to form the intermediate colors we use. Different image channels have been created from the main and intermediate colors.
🔎 As you can see, the main colors Red, Green and Blue are brought together to form Yellow, Cyan and Magenta colors. We’ll see these in the future when we examine the color spaces.
Formation of basic and intermediate colors 🌈
We basically talked about the color concept and the most used RGB structure up to this section. Now that we’ve got the basic structure we need to learn, we can access the color spaces. In image processing, a grayscale image has only one channel. Each pixel value that the image has is valued from 0 to 255. the image contains color according to the pixel values it has. In gray images, instead of colors such as red, green and blue in the RGB channel, the intensity level, ie brightness is handled. Actually, brightness refers to the colorless state of intensity. In color images, the storage space increases because more than one channel is used. The purpose of the color spaces or models I will now describe is often to facilitate color identification. A color space is the process of defining a subspace in the system in which a coordinate system and each color are represented by a single point. The RGB channel is widely used in image processing for color monitors and color video cameras, the CMY and CMYK channels for color printing, and the HSI (HSV) channel is created for people to describe and interpret color. These channels are the leading channels in image processing, so I will talk to you about them today.
RGB Color Channel
RGB is the most widely used color space. In this color model, each color acts as its main spectral components in red, green and blue. In the infrastructure of this model is located a Cartesian coordinate system. The color subspace concerned is examined as this cube, which is frequently used in image processing.
📝When this cube is examined, the RGB primary color values are found in the three corners of the cube, cyan, yellow, and magenda in the other three corners. The values R, G and B are expressed as vectors in the coordinate system. As you can see, in the RGB color space, different colors are located on the cube and in dots. When representing an RGB image with 24 bits, we specify the total number of colors (28) 3 = 16777216, assuming 8 bits are 1 byte. The cube you see above is a solid object containing the number of colors 16777216. To use colors in this cube, there are color codes or values written in specific color models. To use colors in this cube, there are color codes or values written in specific color models. There are many related websites. Examples include w3schools. In OpenCV, which is a very common library in image processing, we will examine how to define an RGB color model and how to extract an RGB histogram of the image.
📃 In OpenCV, the RGB color space is defined as BGR. A Histogram is the name given to a graphic that shows the numbers of color values in an image. If the histogram equality of the values in an image is desired to be expressed on the graph, first The X and y coordinates are specified on the matrix. Then the hist module is shown with imshow( ) by specifying how many boxes there will be. When creating a histogram of an image, we convert the original BGR image to gray.
Create a histogram chart
The original used image was chosen as an image where the RGB color model predominated.
📝 Histogram equalization or equalization is a method to resolve color distribution disorder caused by the fact that color values in a picture are clustered in a specific location. In the graph generated below, the values are clustered in the range 50-100.
(2 8 ) output of histogram chart with 256 boxes 📊
The image is a color image, so the RGB values will be processed and the colors will be separated and the histogram will be balanced for each of the red, green and blue colors.
Create a histogram chart with 32 and 8 boxes
Histogram chart with 32 boxes 📊
Histogram chart with 8 boxes 📊
This generated histogram to convert into a probability distribution function, each value is divided by the sum of these values.
Creating a normalized histogram with the normed=True module
Normalized histogram boxes 📊
HSV | HSI Color Channel
Color models such as RGB, CMY, CMYK do not contain practical terms in terms of human interpretationFor example, no one talks about the color of a house by giving the percentage of the primary colors that make up that color. When we look at a colored object, we express that object with Hue, Saturation, and brightness. For this reason, the concepts of Hue, Saturation and brightness, which make it easy for us to define colors, have been put forward. The name of the HSI space comes from the initials hue, saturation and intensity, which are the English equivalent 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.
The hue, saturation and brightness values used in HSV space are obtained from the RGB color Cube. The brightness value is zero while the color and saturation values for the Black color in the HSV space can take any between 0 and 255. In white, the brightness value is 255.
Conversion from RGB color space to HSV color space
Original : Image Converted to RGB and HSV Space
(a) – hue (b) – saturation (c) – intensity
CMY & CMYK COLOR CHANNEL
In the CMY model, the pigment primary colors, Cyan, winner and yellow, combined in equal amounts, should produce the Black color. In practice, the combination of these colors for printing produces a fuzzy-looking black tone. A fourth color, black, is added, which will reveal the CMYK color model to produce the correct black tone.
As mentioned earlier, this color model is used in image processing to produce hard copies. The equal amount of pigments of the CMY color space should produce the Black color. In order to produce the correct black tone in order to be dominant in printing, black tone was added to the CMY color space and CMYK color space was obtained. In publishing houses, “four-color printing” refers to CMYK, while “three-color printing” refers to the CMY color model.
- C. Gonzalez, Rafael, E. Woods, Richard, Digital Image Processing, Palme Publishing, (Ankara, 2014)
- Retrieved from http://www.kisa-ozet.org/tayf-nedir-kisaca/.
- Dr. Lecturer. Member of Caner ÖZCAN, Karabuk University, BLM429 Introduction to Image Processing, Image Acquisition and Digitization.
- Retrieved from https://www.instructables.com/id/Exploring-Color-Space/
- Retrieved from https://www.fencix.net/isigin-sogurulmasi/
- Retrieved from https://www.eikonal.com.br/8930509-Prismas-especiais
- Retrieved from http://www.atasoyweb.net/Histogram-Esitleme
- Retrieved from https://www.istockphoto.com/tr/foto%C4%9Fraf/rainbow-lorikeet-gm115919863-2434334#/close
- Retrieved from https://people.eecs.berkeley.edu/~sequin/CS184/TOPICS/ColorSpaces/Color_0.html