Abstract: Medical image segmentation is essential for computer-assisted diagnosis since it allows exact extraction of anatomical structures from CT/MRI data. Traditional approaches struggle to handle ...

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.

Background: 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 ...

Python has become one of the most popular programming languages out there, particularly for beginners and those new to the hacker/maker world. Unfortunately, while it’s easy to get something up and ...

Meta Platforms Inc. today is expanding its suite of open-source Segment Anything computer vision models with the release of SAM 3 and SAM 3D, introducing enhanced object recognition and ...

In this post, we will show you how to use MAI-Image-1 for HD image generation on a Windows PC. Microsoft has recently introduced its first text-to-image model built completely in-house. Known as ...

Google Colab, also known as Colaboratory, is a free online tool from Google that lets you write and run Python code directly in your browser. It works like Jupyter Notebook but without the hassle of ...

The Python team at Microsoft is continuing its overhaul of environment management in Visual Studio Code, with the August 2025 release advancing the controlled rollout of the new Python Environments ...

Segmentation of Biomedical Images is based on U-Net. This U-Net implementation using Keras and TensorFlow has varying depth that can be specified by model input.

Abstract: Image segmentation splits the original image into different non-overlapping parts to extract the desired region for various computer vision applications. Diverse methods exist to perform ...