DOI: 10.5061/dryad.nvx0k6dxw
title: MODID: Multispectral oral disease image dataset with segmentaion

description: In oral imaging spectroscopy, tissue data is often collected from resected
 samples, which may not accurately reflect true tissue signatures due to
 preservation chemicals. To overcome this limitation, we aim to capture
 in-vivo spectral signatures for oral diseases. Our dataset comprises
 spectral images acquired in 16 bands, ranging from 460 nm to 600 nm, with
 a resolution of 270x510 pixels. We collected these images from 91
 volunteers, including 15 healthy individuals and 78 patients with various
 oral health conditions. Detailed patient history records are also included
 for each case. This publicly available multispectral dataset holds great
 potential for advancing spectroscopy diagnosis. By leveraging artificial
 intelligence in conjunction with this robust spectral image repository,
 more accurate and insightful outcomes can be achieved in the analysis and
 diagnosis of oral diseases.  Access to this comprehensive oral
 health dataset contributes to enhancing our understanding of tissue
 spectroscopy variability and facilitates further research in the field of
 disease analysis.
description: # MODID: Multispectral oral disease image dataset with segmentation The
 dataset consists of 244 samples from 91 patients. Each sample includes
 both unprocessed and processed multispectral files. Each datacube consists
 of 16 bands in the visible range, stacked to form the complete cube.
 spectral range = 460 nm to 600 nm bands = 16 band frequency (in nm) = {
 460.480990, 465.215098, 474.119400, 482.578653, 492.599330, 504.367465,
 512.469275, 521.987657, 533.930667, 541.203169, 551.996143, 559.719978,
 570.133100, 579.729623, 585.387574, 595.018809 } ## Description of the
 Data ### I. Unprocessed Data The unprocessed data folder comprises of : *
 **Mosaiced raw data** capturing the full sensor size at 2048 × 1088
 resolution, along with its corresponding context file; given as (image.raw
 , acquisition_description.xml and image.raw.xml) * The **context file**
 encompasses vital components, including: 1. **The calibration file** is
 specific to the camera used and is produced during manufacturing to ensure
 accurate spectral data. This file comes with the camera and is used as an
 input to the IMEC DataCapture software before data collection begins. 2.
 **Black Reference File:** This file is used to compensate for dark noise
 during data capture. 3. **White Reference File**: The white reference file
 is employed to generate corrected data spectra and is crucial for ensuring
 the accuracy of spectral data. It accounts for fixed parameters like the
 light source, and the distance between the light source, camera, and the
 object under test. Given the consistent setup in our data collection, any
 of the provided white reference files can be used with the data. In
 regular intervals white reference captures are undertaken as part of
 quality assurance to ensure the reliability and consistency of the
 spectral measurements. 4. **Optical Setup File:** The optical setup file
 contains essential details about the optical configuration used during
 data capture, which is crucial for ensuring accurate spectral data for
 example focal length of the camera and so on. 5. **Context Description
 File:** This file includes comprehensive information about the camera
 system, such as system ID, data type, format, and other pertinent details
 necessary for understanding and processing the captured data. ### II.
 Processed Data In contrast to the unprocessed data, which includes raw
 data files along with their corresponding calibration files, the processed
 data undergoes a different treatment. The calibration files are applied to
 the raw data during export, resulting in a demosaiced spectral data cube
 featuring 16 bands spanning the range of 460 nm to 600 nm. This processed
 dataset exhibits a spectral resolution of 10-15 nm and a spatial
 resolution of 270x510 pixels. Multispectral images often come with .hdr
 (Header) files that contain vital metadata and parameters. However, to
 access the actual pixel values or spectral data, a separate .raw file is
 required. The .hdr file serves as a descriptor, outlining the structure of
 the data, while the .raw file holds the binary pixel values. Together,
 these files provide comprehensive information about the multispectral
 image, also known as the ENVI format (.hdr with .raw). You can use any
 ENVI software available in the market to open the data. There is also a
 python script with the dataset that provides an example to load data. ###
 RGB | Mask In addition to the processed .hdr and .raw files, the dataset
 also includes RGB images and a binary mask for the samples. These RGB
 images provide a visual representation of the data, combining the spectral
 bands to create a color image. The binary mask, on the other hand,
 identifies specific regions of interest within the sample. Together, these
 RGB images and binary masks complement the multispectral data, enabling
 visual interpretation and further analysis of the dataset. ### Description
 of the file structure Under the main folder MODID there are FOUR folders
 named unprocessed, processed, rgb, and mask, along with a patient
 information Excel sheet named MODID_DESCRIPTOR.xlsx and a readme file. ```
 |- Data/ | |- Processed/ | | |- image_number.hdr | | |- image_number.raw |
 |- Unprocessed/ | | |- image_number (image.raw ,
 acquisition_description.xml and image.raw.xml) | | |- context | | | |-
 calibration_file (CMV2K-SSM4x4-460_600-15.7.15.13.xml) | | | |-
 white_reference (white_reference.raw and white_reference.xml) | | | |-
 dark_reference (dark_reference.raw and dark_reference.xml) | | | |-
 optical_setup (optical_setup.xml) | | | |- context_description.xml |- rgb/
 | |- image_number.png |- mask/ | |- image_number.png |- code/ | |-
 Band_image_generation.py | |- spectrum_generation.py ``` ###
 MODID\_DESCRIPTOR.xlsx The Excel spreadsheet named
 MODID_DESCRIPTOR.xlsx," there are two sheets named **Image ID **and
 **Patient data**. * **Image ID** sheet records the names of the image
 samples and is maintained to record which image belongs to which patient.
 Here each image sample is associated with a specific Patient ID in the
 Image ID column. Additionally, the Image Number column indicates the
 number of samples collected for each patient. * **Patient data** sheet
 provides patient information such as the Patient ID (to relate to the
 image id sheet), Gender, Age range, and patient habits such as smoking.
 Furthermore, the sheet includes the patient's diagnosis and displays
 RGB images of the collected samples for each patient. ### Code The code
 uses key libraries for spectral data generation, such as NumPy,
 Matplotlib, and Spectral. There are two files: 1. Band image genration
 - This file has the code to read a multispectral data from the MODID
 dataset and display the spectral images per band. 2. spectrum generation -
 This file is used to generate the mean spectrum over the region of
 interest. The region of interest is taken from the mask png and overlayed
 upon the processed data, to extract the spectral bands only for the area
 of interest. This is then averaged over the pixel area per band to
 generate a spectrum. ### Software  **HSI Mosaic** software from IMEC
 (camera sensor manufacturer) was used for data acquisition. **Label
 Studio** software was used for annotation process and mask generation.