Stable Segmentation of Digital Image

Robust Potato Color Image Segmentation using Adaptive Fuzzy Inference System

Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphological operators. The proposed potato color image segmentation is robust against variation of background, distance and ...

High Performance Implementation of Fuzzy C-Means and Watershed Algorithms for MRI Segmentation

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

High Performance Implementation of Fuzzy C-Means and Watershed Algorithms for MRI Segmentation

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

FPGA-based of Thermogram Enhancement Algorithm for Non-destructive Thermal Characterization

Evaluation of methods of co-segmentation on PET/CT images of lung tumor: simulation study

Introduction: Lung cancer is one of the most common causes of cancer-related deaths worldwide. Nowadays PET/CT plays an essential role in radiotherapy planning specially for lung tumors as it provides anatomical and functional information simultaneously that is effective in accurate tumor delineation. The optimal segmentation method has not been introduced yet, however several ...

Unsupervised Texture Image Segmentation Using MRFEM Framework

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...