Lung Nodule Segmentation Using Adaptive Thresholding and Watershed Transform
Objective
This study introduces a method for lung nodule segmentation using adaptive thresholding and watershed transform techniques. Pre-processing includes histogram equalization and noise filtering. Segmentation utilizes edge masks, morphological operations, and marker-controlled watershed. Lesion diameter is measured to identify abnormal nodules. The technique's accuracy is validated, showing success in detecting and characterizing lung nodules.
Abstract
This study presents a robust approach for lung nodule segmentation utilizing adaptive thresholding and watershed transform techniques. The process begins with the input of a Lung CT image followed by pre-processing steps including histogram equalization, noise filtering, and thresholding. Adaptive thresholding and morphological operations are then applied to enhance nodule delineation, while Sobel edge mask, opening, and closing operations further refine the segmentation. Marker-controlled watershed segmentation is employed to isolate the lung nodules and eliminate extraneous regions. Subsequently, lesion diameter calculation is performed using a connected components algorithm to differentiate between normal and abnormal lesions, with any nodules exceeding 3 mm in diameter classified as abnormal. The accuracy of the segmentation process is evaluated, demonstrating the effectiveness of the proposed methodology in accurately detecting and characterizing lung nodules.
Keywords: Lung nodule, Pre-processing, Watershed segmentation, Adaptive Thresholding.
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Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
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
Disk:
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
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· 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
Paper Publishing
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