GE HealthCare · Summer 2027

Image Quality Intern: a project built for this posting

The intern works with senior engineers on GE HealthCare's surgical X-ray imaging products, tuning image-processing algorithms to improve contrast, clarity and detail. They gather evaluation data in imaging labs, collect feedback from clinical imaging specialists, document designs, and help verify features tied to exposure control and visualization.

Company: GE HealthCare
Posting title: Image Quality Intern - Summer 2027
Location: Salt Lake City, UT
Season: Summer 2027
Majors named: Electrical Engineering, Biomedical Engineering, Physics, Chemistry, Life Sciences

Checked open on the employer's careers site on 2026-10-16. Read the full posting ↗

What this role is really testing

Image processing algorithm implementationQuantitative image quality evaluationPython programming for scientific computingSignal-to-noise and contrast analysisTechnical documentation of design decisionsData visualization of experimental resultsSystematic parameter tuning and benchmarking

The project

X-Ray Image Quality Analyzer: Contrast & Noise Tuning Pipeline

A Python script that loads a grayscale medical-style phantom image, applies and compares multiple image processing filters (histogram equalization, CLAHE, unsharp masking) across tunable parameters, then outputs a side-by-side visual report with quantitative metrics (SNR, contrast ratio, sharpness score) for each result. This maps directly to the role's core work of tuning image processing algorithms and documenting image quality results with technical rigor.

Using Python with NumPy, OpenCV, and Matplotlib, load a free DICOM or grayscale PNG phantom image (RSNA or similar open dataset), define regions of interest for signal and background, and compute baseline image quality metrics: signal-to-noise ratio (mean/std ratio), Michelson contrast between bright and dark ROIs, and a sharpness score using the variance of the Laplacian.
Implement three enhancement functions, histogram equalization, CLAHE (with a grid of clipLimit values from 1.0 to 4.0), and unsharp masking (with a range of radius and strength parameters), and run each variant through the same metric functions, storing all results in a structured dictionary so every parameter combination has a corresponding SNR, contrast, and sharpness score.
Generate a single PDF or PNG report using Matplotlib: a top row showing the original image beside the best-scoring variant of each method (selected by highest composite score), a bottom row of bar charts comparing SNR, contrast, and sharpness across all variants, and a text annotation block summarizing which algorithm and parameter set won each metric category, written in plain engineering language as if presenting findings to a team manager.

Build this project for free →

Free account. Provieo walks you through it and turns it into a page with a link you can put on your application. This posting comes with you.

Questions

Do I need to know how to code?

No. For roles like this one, Provieo builds the project as a live case-study page: slides, tables and findings you fill in. You bring the thinking.

Where did this project come from?

Provieo read the GE HealthCare posting and generated it on 2026-10-16. If you paste the same posting yourself you may get a different project, so two applicants don't show up with the same one.

Is Provieo connected to GE HealthCare?

No. This is an independent project idea based on a public posting. It does not guarantee an interview.