An Effective Occipitomental View Enhancement Based on Adaptive Morphological Texture Analysis

Chondro, Peter; Hu, Hao-Chun; Hung, Hsuan-Yen; Chang, Shin-Yuan; Li, Lieber Po‐Hung; Ruan, Shanq-Jang

doi:10.1109/jbhi.2016.2593455

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…10 Recently, there is emerging evidence suggesting that using deep learning models in the diagnosis of maxillary sinusitis on Waters’ view radiograph was associated with superior accuracy. 23–26 In our study, we found that sinus plain films had very high sensitivity in diagnosing both fungal and bacterial maxillary sinusitis (100% and 96.2%, respectively). There are two possible reasons.…”

Section: Discussionmentioning

confidence: 56%

Combining sinus plain film and sinus ultrasound as a screening tool for maxillary fungal sinusitis

Hung

Kuan

et al. 2022

Journal of the Chinese Medical Association

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Background: Early identification of fungal sinusitis remains a challenge. Previously, we observed a high false negative rate of using A-mode ultrasound to diagnose maxillary fungal sinusitis. This study aims to assess the accuracy of the diagnosis of fungal maxillary sinusitis using sinus plain film and ultrasound. Methods: The screening criteria is defined as the combination of a positive sinus plain film and a false negative sinus ultrasound. We retrospectively reviewed preoperative imaging of patients with fungal sinusitis and unilateral bacterial sinusitis of the maxillary sinus undergoing functional endoscopic sinus surgery from May 2013 to December 2019 in our hospital and evaluated the diagnostic performance of this screening method. Results: Forty-eight patients were included. Twenty-two and 26 patients were diagnosed with fungal sinusitis and bacterial sinusitis, respectively. Sixteen patients (72.7%) with fungal sinusitis presented with a false negative sinus ultrasound and met our screening criteria for fungal sinusitis. The screening criteria reached significance in the receiver operating characteristic curve analysis (p < 0.001). The area under the curve was 0.829. The sensitivity, specificity, and accuracy are 72.7%, 93.2%, and 88.4%, respectively. Conclusion: A high false negative rate of sinus ultrasound in patients with fungal sinusitis was found. A positive sinus plain film combined with a false negative sinus ultrasound can potentially become an easy and cost-effective screening tool for diagnosing fungal maxillary sinusitis before consideration of computed tomography scanning.

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Section: Discussionmentioning

confidence: 56%

Combining sinus plain film and sinus ultrasound as a screening tool for maxillary fungal sinusitis

Hung

Kuan

et al. 2022

Journal of the Chinese Medical Association

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“…Iris Detection: while the images have obstruction, visual noise and singular levels of illumination, still in this condition iris can be detected. It eliminates the lighting reflections, eyelids and eyelashes obstruction [15]. It also accepts the images with tapering eyelids or eyes that are gaze away with the help of wavelet algorithm.…”

Section: Algorithm For Detection and Segmentationmentioning

confidence: 99%

Novel Technique for Iris Detection in Image Processing

.¹

2018

IJRET

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An automated method utilized for biometric identification which includes various mathematical pattern-recognition methods in it is known as the iris recognition method. The videos of the irises of various individual's eyes are studied in this technique. The complex random patterns present within this approach are single, constant moreover can as well viewed from a particular distance. In the base paper, Circular-Hough Transformation is applied with canny edge detection. The GLCM algorithm is applied which will extract the contrast, energy, entropy and heterogeneity of the detected iris has been calculated. To increase the accuracy of iris detection and reduce execution time, improvement in existing GLCM algorithm, feature extraction technique is being proposed. The proposed improvement will be based on applying structural tensor algorithm and improved GLCM for contrast detection.

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RETRACTED CHAPTER: Texture Analysis in Skull Magnetic Resonance Imaging

Viloria

Hoz

Lezama

2020

Advances in Intelligent Systems and Computing

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raumatic brain injury (TBI) represents a serious public health problem worldwide. It is the most common cause of death and disability in the young population (aged 15-45 years), with major family, social and economic implications [1]. In medical terms, the human body can be studied as an object. The reconstruction of bone structures after physical damage generated by such an unfortunate event as disease or trauma can range from the implementation of prostheses to the engineering of artificial bone implants [2]. To make a virtual or physical model of any human anatomy, it must first be captured in three dimensions in a way that can be used by computational processes. Most hospital scanners capture data from the entire body both internally and externally. These machines are typically medical imaging devices capable of scanning the entire human body, among which, the most common is the magnetic resonance imaging (MRI) equipment [3]. The goal of the research is to analyze the texture in magnetic resonance imaging and its relationship to bone mineral content (BMC) using simple linear regression.

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An Effective Occipitomental View Enhancement Based on Adaptive Morphological Texture Analysis

Cited by 6 publications

References 16 publications

Combining sinus plain film and sinus ultrasound as a screening tool for maxillary fungal sinusitis

Combining sinus plain film and sinus ultrasound as a screening tool for maxillary fungal sinusitis

Novel Technique for Iris Detection in Image Processing

RETRACTED CHAPTER: Texture Analysis in Skull Magnetic Resonance Imaging

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