Analysis of Various Image Segmentation Techniques on Retinal OCT Images

Project Code :TMMAIP415


The primary objective of this project is to systematically evaluate and compare different image segmentation techniques applied to Retinal Optical Coherence Tomography (OCT) images. This analysis aims to enhance our understanding of the performance and suitability of various segmentation methods for accurately delineating retinal structures and anomalies within OCT images


Image segmentation is one of the most important processes involved in image processing. Segmenting an image is breaking it up into smaller pieces, or segments. These are the areas where it is most helpful because processing the complete image would be inefficient for tasks like object recognition or image compression. 

Image segmentation is the process of dividing an image's elements for further processing. This article looked at a number of image segmentation techniques, including the threshold approach, edge-based method, and clustering-based method. The segmentation method that is most effective for separating images in OCT images is clustering-based segmentation. 

To reduce the speckle noise during preprocessing, the wiener filter approach is used. With regards to the threshold, edge-based (Sobel, Canny, Robert's), and clustering-based segmentation techniques, calculate MSE (mean square error) and PSNR (peak signal to noise ratio) values for the segmented image quality.

Keywords— Clustering; Edge detection; Image segmentation; Region-based; Threshold

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

Block Diagram


Software: Matlab 2020a 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:

               o  Acquisition

               o  Image enhancement

               o  Image restoration

               o   Color image processing

               o  Image compression

               o   Morphological processing

               o   Segmentation etc.,

·   How to extend our work to another real time applications

·   Project development Skills

               o   Problem analyzing skills

               o   Problem solving skills

               o   Creativity and imaginary skills

               o   Programming skills

               o   Deployment

               o   Testing skills

               o   Debugging skills

               o   Project presentation skills

               o   Thesis writing skills

Demo Video

Request Video

Related Projects

Final year projects