---
task_categories:
- image-segmentation
tags:
- retina
- multispectral
pretty_name: retina-masks-msi
size_categories:
- 100M<n<1B
---
## Retina Masked Dataset
`Control`
`Non-Healthy`

#### Overview
This dataset contains Retina Multispectral images in __*Diseased*__ and __*Control*__ classes that also has masked different parts of Retina.

Already segmented masked are listed below:

- Vessels
- Optic Disk
- Macula
- Background

## Region Specific Labels
`region_specific_label.csv`
#### Overview
For each image, a region-specific label is generated and stored in a table called region_specific_label.csv. This table contains four distinct columns, each representing a different region. The text values in this table are coded as follows:

- `d` indicates __*Diseased*__
- `c` indicates __*Control*__

__TODO__:

Dataset module for this dataset is going to be added:

- retina-masked-dataset.py
This class will read data from dataset and you can use it in pytorch with ease.
> Metadata Included

## Variance Analysis Data
`data_variance_analysis.csv`
#### Overview
This dataset is designed for variance analysis of retinal health using phasor analysis of multispectral images. It supports a research project focused on comprehensive region-specific retinal health assessment using machine learning techniques.


#### Feature Categories

#### 1. Harmonic Features (h1-h4)
Captures phasor analysis (g, s) values across 4 harmonic levels under consistent conditions.

#### 2. Normalization - Phasor Features
Provides normalized representations for baseline comparison.

#### 3. Phasor-RGB 
Spectral analysis using RGB channels (bands 2, 5, 9).

#### 4. Phasor-MSI Features
Full multispectral phasor analysis across all 12 spectral bands.

#### 5. Average Features (RGB & MSI)
Direct spectral intensity measurements.

> Metadata Included