Image segmentation using the Otsu method in Dental X-rays

Setianingrum, Anif Hanifa; Rini, Amanda Satya; Hakiem, Nashrul

doi:10.1109/iac.2017.8280611

Cited by 9 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the tooth is segmented using multistage adaptive thresholding (MAT). Although, for the identification of tooth decay, the Otsu method was employed for automatically setting threshold values without human intervention, it was found that images with image enhancement demonstrated larger threshold values and can be utilized by researchers for discriminant analysis [ 109 ]. To improve disease pattern recognition, Ali et al proposed a fuzzy clustering algorithm based on neutrosophic orthogonal matrices for segmentation.…”

Section: Approaches To Dental Disease Diagnosis Using X-ray Imagingmentioning

confidence: 99%

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

et al. 2023

View full text Add to dashboard Cite

Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

show abstract

Section: Approaches To Dental Disease Diagnosis Using X-ray Imagingmentioning

confidence: 99%

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

et al. 2023

View full text Add to dashboard Cite

show abstract

Detection of periodontal bone loss in mandibular area from dental panoramic radiograph using image processing techniques

Vigil

Bharathi

2021

Concurrency and Computation

View full text Add to dashboard Cite

Medical imaging plays the vital role in diagnosis of abnormalities. Periodontitis is a common chronic inflammatory disease damaging the soft tissue that untreated can lead to loss of bone, which supports the teeth. The severity of periodontitis is correlated to pocket depth in alveolar area. Even if analyses of the pocket depth can be manually performed, an automatic assistive tool can drastically help radiologists conduct more accurate analyses. In the proposed research, image processing algorithms were developed to compute pocket depth and diagnose the periodontitis. Radiologists manually segmented 350 panoramic radiographic images, dividing them into normal and periodontitis. The same dataset is used in our work to validate the Classification algorithm. The images are preprocessed with median filter and histogram equalization to improve the contrast and then segmented using two‐dimensional‐Otsu thresholding method into teeth and bony area in the mandibular region. Normal pocket depth of 3 mm as reported by American Academy of Periodontology is equivalently converted to pixel height in the radiographic images. Based on this pocket depth rule based classification method classifies the images into normal and periodontitis. The proposed work achieved 91.34% accuracy, 92.8% sensitivity, and 95.47% F‐score in classifying the dental panoramic radiography.

show abstract

A Survey on Image Processing Techniques for Detection of Cavities in Dental X-ray Images

Geethasree¹,

Sri²,

Sravani³

et al. 2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Image segmentation using the Otsu method in Dental X-rays

Cited by 9 publications

References 4 publications

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

Detection of periodontal bone loss in mandibular area from dental panoramic radiograph using image processing techniques

A Survey on Image Processing Techniques for Detection of Cavities in Dental X-ray Images

Contact Info

Product

Resources

About