# Combining contour-based and region-based in image segmentation

## TL;DR

This paper introduces a new image segmentation method that combines region-based and contour-based techniques to improve accuracy in applications like medical imaging and object detection.

## Contribution

The novel contribution is an optimized clustering approach that integrates edge detection, Gabor wavelets, and color frequencies for improved image segmentation.

## Key findings

- The proposed method outperformed other wavelet and clustering techniques in segmentation metrics like SNR, PSNR, and MCC.
- Optimizing the number of clusters significantly enhances the performance of image segmentation techniques.
- Combining region-based and contour-based methods improves detection and localization in segmentation-based applications.

## Abstract

Background: This paper presents an optimized clustering approach applied to image segmentation. Accurate image segmentation impacts many fields like medical, machine vision, object detection. Applications involve tumor detection, face detection and recognition, and video surveillance.

Methods: The developed approach is based on obtaining an optimum number of clusters and regions of an image. We combined Region-based and contour-based approaches. Initial rough regions are obtained using edge detection. We have used Gabor wavelets for texture classification and spatial resolutions. Color frequencies are also used to determine the number of clusters of the Fuzzy c-means (FCM) algorithm which gave an optimum number of clusters or regions.

Results: We have compared our approach with other similar wavelet and clustering techniques. Our algorithm gave better values for segmentation metrics like SNR, PSNR, and MCC.

Conclusions: Optimizing the number of clusters or regions has a significant effect on the performance of the image segmentation techniques. This will result in better detection and localization of the segmentation-based application.