An accurate thresholding-based segmentation technique for natural images

Segmentation is a process of dividing an image into distinct regions with the aim to extracts object of interest from the background. The traditional thresholding and clustering segmentation techniques that were widely used are Otsu and K-means, respectively. However, the segmentation process becomes more challenging for segmenting natural images. Both Otsu and K-means methods failed to produce good quality of segmented areas under natural environment due to the complex background and non-uniform illumination on the images. Therefore, this paper proposed an improved thresholding-based segmentation with inverse technique (TsTN) that was able to partition natural images. A comparison between Otsu, K-means and TsTN techniques was conducted using colour-based image processes on the quality of the segmented images. The analysis results showed that TsTN has the ability to produce good quality of segmented images. Furthermore, this improved technique was proved to be more accurate than the traditional thresholding and clustering techniques.