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Avascular Necrosis (AVN) || Early Detection, Better Outcomes || ~xRay Pixy

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Avascular Necrosis (AVN) is a condition where blood flow to the bone is reduced, causing bone cells to die. This leads to pain, joint damage, and difficulty in movement, especially in the hip. Early diagnosis and proper treatment can prevent permanent bone damage and improve quality of life. Video Chapter: AVN 00:00 Introduction 00:45 What is AVN? 01:55 About Bone Tissue 02:49 AVN Causes 03:38 AVN Symptoms 04:11 AVN Diagnosis 04:56 AVN of femoral head 05:33 How AVN Develops 07:28 Conclusions #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python #optimizationproblem #optimizationalgorithms 
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Multi-Block Local Binary Pattern || Calculate LBP Corner Pixel Values || 
https://youtu.be/o8qfJWQ_FG0

 Local Binary Patterns (LBP) is a simple and efficient technique used in image processing to describe the texture or patterns within an image. LBP is widely used for applications like face recognition and texture classification since it is easy to compute and very effective at capturing the texture in photos. Step How LBP WORKS:

  1.  For each pixel in the image, LBP looks at the pixel’s neighbors, typically the 8 pixels surrounding it in a 3x3 grid.
  2. LBP compares each of these neighboring pixels with the center pixel. If the neighboring pixel has a value greater than or equal to the center pixel, it's marked as 1; otherwise, it's marked as 0. This comparison forms a binary number for the pixel.
  3.  The binary number is then converted into a decimal value. This value represents the texture pattern at that pixel.
  4. By doing this for every pixel in the image, LBP creates a new image that highlights the texture information.
Difference Between LBP and MB-LBP 

  • LBP compares individual pixels to a center pixel in a small neighborhood (usually 3x3).
  • MB-LBP compares the average intensity of blocks (groups of pixels) to the average intensity of a center block. (Divide into Blocks, Compare Block Averages, Create Binary Pattern and Convert to Decimal)

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