Threshold-based segmentation by selecting a target color vector in one of six color spaces (RGB, HSV, CIELAB, CIEXYZ, YCbCr or YIQ (NTSC)) and isolating pixels within a user-specified tolerance.

This repository contains a MATLAB-based image processing project focused on leaf segmentation and morphometric analysis. The work was completed as part of the MOD002643 – Image Processing module at ...

This study aims to investigate the application of visual information processing mechanisms in the segmentation of stem cell (SC) images. The cognitive principles underlying visual information ...

An AI algorithm converts 2D electron microscope images into accurate 3D structures, cutting analysis time and cost to one-eighth while preserving precision. The newly developed algorithm requires ...

Abstract: The U-Net algorithm, with its unique network structure and excellent performance, has become a classic algorithm in the field of image semantic segmentation. However, there are still some ...

A new artificial intelligence (AI) tool could make it much easier-and cheaper-for doctors and researchers to train medical imaging software, even when only a small number of patient scans are ...

Medical image segmentation is at the heart of modern healthcare AI, enabling crucial tasks such as disease detection, progression monitoring, and personalized treatment planning. In disciplines like ...

Pore space in tight sandstone formation is very complex with micro-scale and nano-scale pores/throats, the multi-scale characteristics needs to be considered for the construction of microscopic pore ...

ABSTRACT: Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering ...