Implementation of PSO & HB Image Enhancement Techniques For Tumor Detection (original) (raw)

Abstract

Image enhancement is a mean as the improvement of an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Many images such as medical images, remote sensing images, electron microscopy images and even real life photographic pictures, suffer from poor contrast. Therefore there are many techniques of image enhancement in image processing such as Fast Ostu's method, Histogram equalization (HE) method, Particle swarm optimization (PSO) , Honey Bee (HB) In this paper, a new approach to automatic image enhancement using HB is implemented by specifying intensity of the edges pixels and also earlier reported PSO results were used. Further comparatively analysis is performed between HB and PSO results. The obtained results indicate that the proposed HB yields better results in the terms of both the maximization of number of the pixels in the edges and peak signal to noise ratio (PSNR). Computational time is also relatively small in the HB as compared to the PSO.

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What are the key advantages of using Honey Bee Algorithm for image enhancement?add

The research finds that the Honey Bee Algorithm achieves a fitness value significantly higher than PSO, yielding a more effective optimization for image enhancement tasks. Furthermore, the computational time for HB-based enhancement was reduced to 10.785 seconds compared to PSO's 94.786 seconds.

How does Honey Bee Algorithm compare in computational efficiency to Particle Swarm Optimization?add

The paper demonstrates that the Honey Bee Algorithm outperforms PSO in computational efficiency, taking only 10.785 seconds for enhancement tasks. This efficiency suggests that the HB algorithm may offer considerable advantages for real-time applications in medical image processing.

What metrics were used to evaluate the performance of image enhancement techniques?add

Performance was evaluated using Peak Signal-to-Noise Ratio (PSNR) values, computational time, and the number of edge pixels detected. The findings revealed that the HB algorithm consistently produced higher PSNR and edge counts compared to the PSO method.

What is the primary objective of the implemented HB enhancement method?add

The primary objective of the Honey Bee enhancement method is to maximize the total number of pixel edges to enhance image detail. This approach helps in better visualization for tumor detection in medical images.

What role does swarm intelligence play in the proposed image enhancement methods?add

Swarm intelligence facilitates optimization in the proposed enhancement methods by simulating natural behaviors of social insects, such as honey bees, leading to robust performance in image processing tasks. Notably, the HB algorithm demonstrated a simpler and more flexible approach compared to traditional swarm algorithms.

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