Clustering Based Blood Smear Image Segmentation Techniques

Project Code :TMMAIP374


This work examines different clustering-based image segmentation algorithms that are currently in use. Some of them are K means, Watershed, Edge based, Region growing


This work examines different clustering-based image segmentation algorithms that are currently in use. Image segmentation involves extracting valuable information from an image; this might include locating objects as you move about the region or looking for abnormalities in medical images. Due to the fact that image pixels are typically unlabeled, clustering is a widely used method for analyzing them. 

All of the major clustering algorithms have been reviewed, namely K-Means Clustering based, Watershed, Edge based and Region Based Segmentation Methods. Accuracy of each technique is compared with the existing techniques.

Keywords: Image Segmentation, K-Means Clustering, Watershed, Edge Based Segmentation, Region Based Segmentation

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram


Software & Hardware Requirements:

Software: Matlab R2020a or above


Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016


Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support


Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space


Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • How to start with MATLAB
  • About Matlab language
  • Matlab coding skills
  • About tools & libraries
  • Application Program Interface in Matlab
  • About Matlab desktop
  • How to use Matlab editor to create M-Files
  • Features of Matlab
  • Basics on Matlab
  • What is an Image/pixel?
  • About image formats
  • Introduction to Image Processing
  • How digital image is formed
  • Importing the image via image acquisition tools
  • Analyzing and manipulation of image.
  • Phases of image processing:
    • Acquisition
    • Image enhancement
    • Image restoration
    • Color image processing
    • Image compression
    •  Morphological processing
    • Segmentation etc.,
  • How to extend our work to another real time applications
  • Project development Skills
    • Problem analyzing skills
    • Problem solving skills
    • Creativity and imaginary skills
    • Programming skills
    • Deployment
    • Testing skills
    • Debugging skills
    • Project presentation skills
    • Thesis writing skills

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