Segmentation of Natural Images Using an Improved Thresholding-Based Technique

Abstract

This paper investigates fundamental problems in image segmentation using traditional segmentation techniques and proposed an improved technique for segmenting images captured under natural environment. Image segmentation refers to a process of partitioning a digital image into multiple regions with the aim to extracts object of interest from the background. However, the segmentation process is very challenging especially for experiment which conducted in outdoor environment. It is difficult to produce a significant threshold value which required for segmenting images due to non uniform illumination and difference of reflection. Different illuminations may produce different colour intensity of the object surface and thus lead to inaccurate segmented images. The widely used traditional thresholding and clustering segmentation techniques are Otsu and Fuzzy c-means (FCM), respectively. Both traditional methods were unable to produce good quality segmented areas due to the complex background and non-uniform illumination of images captured under natural environment. Therefore, this paper proposed an improved thresholding-based segmentation integrated with an inverse technique (TsTN) that was able to partition natural images correctly. The three segmentation techniques were implemented on fruit images and their performance was evaluated based on the ground truth. The segmentation techniques performance was compared quantitatively using evaluation method, Rand Index (RI). The analysis results showed that TsTN has the ability to produce good quality segmented images. Furthermore, this segmentation technique was proven to be more accurate than the traditional thresholding and clustering techniques.