Optical coherence tomography image dataset of genuine and faux leather

Published: 10 November 2023| Version 1 | DOI: 10.17632/bx2xwd9gsh.1
Metin Sabuncu, Hakan Ozdemir


We have recorded Optical Coherence Tomography (OCT) images of both genuine and faux leather samples. The genuine leather samples comprised leather from small and large ruminants. The data is organized into three folders: FauxLeatherSamples, LargeRuminantSamples, and SmallRuminantSamples, each containing 5 distinct samples. Therefore, a total of 15 different samples (5 small ruminants, 5 large ruminants, and 5 artificial/faux leathers) were scanned at different points on their surfaces. Under the FauxLeatherSamples folder, OCT images corresponding to the 5 separate artificial leather samples are stored. To ensure approximate consistency in the data from the samples, the OCT scans were fixed to 2 mm from each image and saved in the same PNG format. We are making this labeled OCT leather type dataset publicly available. The dataset consists of raw OCT images stored in 15 separate folders, all in the same format. Each folder contains only OCT scans taken randomly over a single genuine or faux leather sample. The OCT scans corresponding to the genuine leather samples are found under the folders named LargeRuminantSamples and SmallRuminantSamples. Furthermore, under these folders, subfolders labeled 1 to 5 contain OCT scans of five different leathers, all of which are genuine. The scans are raw OCT-B image files, captured using ThorImage software on the Thorlabs OCT imaging system. No additional image processing or filtering has been performed on the raw OCT images. Each OCT scan is a separate image with the date and time of the scan stamped within the file name. For instance, under the folder LargeRuminantSamples, within Folder 1, there are OCT scans of the genuine leather sample 1, originating from a large ruminant leather piece. The file named "OCT Image 060315_12242021.png" in this folder corresponds to the OCT scan of the first leather sample taken on December 24, 2021, at 06:03:15.


Steps to reproduce

Utilizing a Thorlabs CAL110C1 imaging system, we captured OCT images, which employs a laser diode featuring a 930 nm central wavelength [1]. This diode emits broadband photons, ensuring the acquisition of images free from speckle interference [2,3,4]. The integral ThorImage OCT software facilitated the imaging process [5]. We placed genuine and faux leather samples in the scanning arm sequentially. An OCT-B scan, a two-dimensional image of the leather samples, was obtained by maneuvering the light beam across the sample and aggregating the associated OCT-A images [6]. A uniform scan length of 2 mm was applied to all OCT-B images across the 15 samples to maintain data consistency [7]. Ensuring compatibility with deep learning algorithms, which typically require a minimum of 100 images per class, we aimed to capture at least a hundred scans for each type of leather. Each sample was separately positioned in the scanning arm, measured in OCT mode, and subsequently stored in an individual folder with a consistent format [8]. The OCT images, raw OCT-B files, were stored utilizing the ThorImage software package [9], with no extra image processing or filtering applied to the obtained data [10]. References: [1] M. Sabuncu and H. Ozdemir, Classification Of Material Type From Optical Coherence Tomography Images Using Deep Learning, International Journal of Optics, 2021. [2] M. Sabuncu, M. Akdoğan, Photonic imaging with optical coherence tomography for quality monitoring in the poultry industry: A preliminary study, Rev. Bras. Cienc. Avic. 17 (2015) 319–324. [3] I. Yılmazlar and M. Sabuncu, Speckle noise reduction based on induced mode Hopping in a semiconductor laser diode by drive current modulation, Opt. Laser Technol., vol. 73, pp. 19–22, 2015. [4] I. Yilmazlar and M. Sabuncu, Implementation of a current drive modulator for effective speckle suppression in a laser projection system, IEEE Photonics J., vol. 7, no. 5, pp. 1–6, 2015. [5] M. Sabuncu, H. Özdemir, Recognition of fabric weave patterns using optical coherence tomography, J. Text. Inst. 107 (2016) 1406–1411. [6] M. Sabuncu, H. Ozdemir, Contactless measurement of fabric thickness using optical coherence tomography, The Journal of the Textile Institute. (2021) 1901457. [7] M. Sabuncu, H. Ozdemir, M. U. Akdogan, Automatic identification of weave patterns of checked and colored fabrics using optical coherence tomography, IEEE Photonics J. 9 (2017) 1–8. [8] A.G. Turk, M. Sabuncu, M. Ulusoy, Evaluation of adaptation of ceramic inlays using optical coherence tomography and replica technique, Braz. Oral Research, vol. 32, pp. 1-10, 2018. [9] M. Sabuncu and H. Özdemir, Recognition of weave patterns of striped fabrics using optical coherence tomography, Fibres Text. East. Eur., vol. 26, no. 3(129), pp. 98–103, 2018. [10] M. Sabuncu, and H Özdemir. "Optical coherence tomography imaging can identify Merino lambs’ wool using automatic machine learning vision." Text. Res. J.93, no. 19-20 (2023)


Dokuz Eylul Universitesi


Optics, Optical Coherence Tomography, Image Database, Image Classification, Leather Good Industry, Leather