Segmentation & Classification of Plasmodium Species Using Image Processing and Machine Learning Techniques

Project Code :TMMAIP376


The main objective of this work is to segment Plasmodium species blood smear using Kapur and Otsu strategy and classifying the type of species using Machine Learning Technique.


Segmentation is one of the important steps for image analysis. Multilevel thresholding image segmentation was more popular in image segmentation. Otsu and Kapur based methods are most popular for multilevel threshold image segmentation. 

Many authors implemented evolutionary algorithms for the optimal multilevel threshold selection based on the above methods. In this work, an efficient approach for multilevel image segmentation has been proposed and implemented based on Kapur and Otsu strategy. 

After the segmentation process, classification will be performed on Plasmodium species using Machine Learning algorithm- Random Forest. The types of plasmodium species are P. Falciparum, P. Malariae and P. Vivax which are collected from Kaggle portal. To check the effectiveness of our method/work, image entropy of Kapur and Otsu strategy and classification results are evaluated.

Keywords: Image Segmentation, Multilevel Thresholding, Kapur and Otsu Strategy, Machine Learning, Classification, Random Forest, Plasmodium Species.

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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