A Two-Stage Automatic Color Thresholding Technique

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2023 Mar 30

PMID 36992072

Authors

Shamna Pootheri

Daniel Ellam

Thomas Grubl

Yang Liu

Affiliations

Soon will be listed here.

Abstract

Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one's focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels. The method is unsupervised, fully automated, and does not need any training or ground-truth data. The performance of the proposed method was evaluated using a printed circuit assembly (PCA) board dataset and the University of Waterloo skin cancer dataset. Accurately performing background suppression in PCA boards facilitates the inspection of digital images with small objects of interest, such as text or microcontrollers on a PCA board. The segmentation of skin cancer lesions will help doctors to automate skin cancer detection. The results showed a clear and robust background-foreground separation across various sample images under different camera or lighting conditions, which the naked implementation of existing state-of-the-art thresholding methods could not achieve.

References

Zou K, Warfield S, Bharatha A, Tempany C, Kaus M, Haker S . Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol. 2004; 11(2):178-89. PMC: 1415224. DOI: 10.1016/s1076-6332(03)00671-8. View

Mason D, LAUDER I, Rutovitz D, Spowart G . Measurement of C-bands in human chromosomes. Comput Biol Med. 1975; 5(3):179-201. DOI: 10.1016/0010-4825(75)90004-9. View

Chicco D, Jurman G . The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics. 2020; 21(1):6. PMC: 6941312. DOI: 10.1186/s12864-019-6413-7. View

Sulaiman A, Omar K, Nasrudin M . Degraded Historical Document Binarization: A Review on Issues, Challenges, Techniques, and Future Directions. J Imaging. 2021; 5(4). PMC: 8320943. DOI: 10.3390/jimaging5040048. View

Venugopal V, Joseph J, Das M, Nath M . DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images. Comput Biol Med. 2022; 148:105852. DOI: 10.1016/j.compbiomed.2022.105852. View

Uslu F, Bharath A . TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation. Comput Biol Med. 2022; 152:106422. DOI: 10.1016/j.compbiomed.2022.106422. View

Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D . Image Segmentation Using Deep Learning: A Survey. IEEE Trans Pattern Anal Mach Intell. 2021; 44(7):3523-3542. DOI: 10.1109/TPAMI.2021.3059968. View

Prewitt J, Mendelsohn M . The analysis of cell images. Ann N Y Acad Sci. 1966; 128(3):1035-53. DOI: 10.1111/j.1749-6632.1965.tb11715.x. View

Mittal H, Pandey A, Saraswat M, Kumar S, Pal R, Modwel G . A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets. Multimed Tools Appl. 2021; 81(24):35001-35026. PMC: 7870780. DOI: 10.1007/s11042-021-10594-9. View

10.

Punn N, Agarwal S . Modality specific U-Net variants for biomedical image segmentation: a survey. Artif Intell Rev. 2022; 55(7):5845-5889. PMC: 8886195. DOI: 10.1007/s10462-022-10152-1. View

11.

Chen S, Zhong L, Qiu C, Zhang Z, Zhang X . Transformer-based multilevel region and edge aggregation network for magnetic resonance image segmentation. Comput Biol Med. 2022; 152:106427. DOI: 10.1016/j.compbiomed.2022.106427. View

12.

Yang J, Wu X, Liang J, Sun X, Cheng M, Rosin P . Self-Paced Balance Learning for Clinical Skin Disease Recognition. IEEE Trans Neural Netw Learn Syst. 2019; 31(8):2832-2846. DOI: 10.1109/TNNLS.2019.2917524. View

13.

Su H, Zhao D, Elmannai H, Heidari A, Bourouis S, Wu Z . Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization. Comput Biol Med. 2022; 146:105618. PMC: 9113963. DOI: 10.1016/j.compbiomed.2022.105618. View

14.

Qi A, Zhao D, Yu F, Heidari A, Wu Z, Cai Z . Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation. Comput Biol Med. 2022; 148:105810. PMC: 9278012. DOI: 10.1016/j.compbiomed.2022.105810. View

15.

Han Q, Wang H, Hou M, Weng T, Pei Y, Li Z . HWA-SegNet: Multi-channel skin lesion image segmentation network with hierarchical analysis and weight adjustment. Comput Biol Med. 2022; 152:106343. DOI: 10.1016/j.compbiomed.2022.106343. View

16.

Fan H, Xie F, Li Y, Jiang Z, Liu J . Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold. Comput Biol Med. 2017; 85:75-85. DOI: 10.1016/j.compbiomed.2017.03.025. View