Autofluorescence Bronchoscopy Image Processing in the Selected Colour Spaces Authors

Finkst, T.; Tasič, J.F.; Zorman-Terčelj, Marjeta; Zajc, Matej

doi:10.5545/sv-jme.2012.350

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…Multi-level Otsu-based thresholding, which improves upon Finkvst's AFB approach [10], roughly partitions I I into background, normal exposure, and overexposed regions. Next, active contour analysis, which derives contours for regions exhibiting weak edge gradients, combines the normal and overexposed regions into a single foreground mask image M fore [12].…”

Section: Arxiv:230312198v1 [Eessiv] 21 Mar 2023mentioning

confidence: 99%

“…An informative frame must show evidence of containing lesions for it to be flagged as containing lesions. Early AFB research relied on a simple Red-to-Green ratio threshold test R G > 0.53 to distinguish lesion and normal areas [16], with Finkvst using the R and G distributions over a frame [10]. Unfortunately, variations in illumination, scope-to-wall distance, and surface ripples make the method unsuitable.…”

Section: Lesion Analysismentioning

confidence: 99%

“…Other uninformative frames are degraded by overexposure, as when the bronchoscope is too close to an airway wall. While a few works have applied computer-based analysis to AFB video, they all have severe limitations [8], [9], [10], [11]: 1) don't distinguish informative and uninformative frames; 2) require manual interaction to identify a lesion frame; and 3) entail manual interaction to define a lesion.…”

Section: Introductionmentioning

confidence: 99%

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Autofluorescence Bronchoscopy Video Analysis for Lesion Frame Detection

Chen

Bascom

Toth

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Because of the significance of bronchial lesions as indicators of early lung cancer and squamous cell carcinoma, a critical need exists for early detection of bronchial lesions. Autofluorescence bronchoscopy (AFB) is a primary modality used for bronchial lesion detection, as it shows high sensitivity to suspicious lesions. The physician, however, must interactively browse a long video stream to locate lesions, making the search exceedingly tedious and error prone. Unfortunately, limited research has explored the use of automated AFB video analysis for efficient lesion detection. We propose a robust automatic AFB analysis approach that distinguishes informative and uninformative AFB video frames in a video. In addition, for the informative frames, we determine the frames containing potential lesions and delineate candidate lesion regions. Our approach draws upon a combination of computer-based image analysis, machine learning, and deep learning. Thus, the analysis of an AFB video stream becomes more tractable. Tests with patient AFB video indicate that ≥97% of frames were correctly labeled as informative or uninformative. In addition, ≥97% of lesion frames were correctly identified, with false positive and false negative rates ≤3%.Clinical relevance-The method makes AFB-based bronchial lesion analysis more efficient, thereby helping to advance the goal of better early lung cancer detection.

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Section: Arxiv:230312198v1 [Eessiv] 21 Mar 2023mentioning

confidence: 99%

Section: Lesion Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Autofluorescence Bronchoscopy Video Analysis for Lesion Frame Detection

Chen

Bascom

Toth

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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“…red/green ratio. 3 Other more recent work has considered standard computer-based image processing methods and/or conventional machine learning techniques: [4][5][6] Unfortunately, these works all have at least one of the following limitations:…”

Section: Introductionmentioning

confidence: 99%

ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video

Chang

Ahmad

Toth

et al. 2023

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

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Lung cancer tends to be detected at an advanced stage, resulting in a high patient mortality rate. Thus, much recent research has focused on early disease detection. Lung cancer generally first appears as lesions developing within the bronchial epithelium of the airway walls. Bronchoscopy is the procedure of choice for effective noninvasive bronchial lesion detection. In particular, autofluorescence bronchoscopy (AFB) discriminates the autofluorescence properties of normal and diseased tissue, whereby lesions appear reddish brown in AFB video frames, while normal tissue appears green. Because recent studies show AFB's high sensitivity for lesion detection, it has become a potentially pivotal method during the standard bronchoscopic airway exam for early-stage lung cancer detection. Unfortunately, manual inspection of AFB video is extremely tedious and error prone, while limited effort has been expended toward potentially more robust automatic AFB lesion detection and segmentation. We propose a real-time deep-learning architecture dubbed ESFPNet for accurate segmentation and robust detection of bronchial lesions in an AFB video stream. The architecture features an encoder structure that exploits pretrained Mix Transformer (MiT) encoders and an efficient stage-wise feature pyramid (ESFP) decoder structure. Segmentation results derived from the AFB airway-exam videos of 20 lung cancer patients indicate that our approach gives a mean Dice index = 0.756 and average Intersection of Union = 0.624, results that are superior to those generated by other recent architectures. In addition, our method enables a processing throughput of 27 frames/sec. Thus, ESFPNet gives the physician a potential tool for confident real-time lesion segmentation and detection during a live bronchoscopic airway exam. Moreover, our model shows promising potential applicability to other domains, as evidenced by its state-of-the-art (SOTA) performance on the CVC-ClinicDB and ETIS-LaribPolypDB datasets and its superior performance on the Kvasir and CVC-ColonDB datasets.

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“…In several tasks, processing of visual content consists of image processing operations which include acquisition of image, pre-processing, segmentation procedure (performed with GrowCut (GW), random walker (RW) and other methods), attribute (feature) extraction and classification [2]. Improvement of existing analyses of medical images leads to better medical diagnosis [6]. This article represents a new effort to establish machine-supported analysis of medical images related to erythema migrans, an early manifestation of Lyme borreliosis.…”

Section: Introductionmentioning

confidence: 99%

Supervised Visual System for Recognition of Erythema Migrans, an Early Skin Manifestation of Lyme Borreliosis

Čuk¹,

Gams²,

Mozek³

et al. 2014

SV-JME

Self Cite

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Lyme borreliosis is the most common human tick-borne infectious disease in the northern hemisphere, occurring predominantly in temperate regions of North America, Europe and Asia. The disease's most frequent manifestation is erythema migrans, a skin lesion that appears within days to weeks of a tick bite. Early recognition of the lesion is important since it enables proper management and thus prevention of later consequences of the disease which can hamper normal life. In this article, a novel visual system for recognition of erythema migrans is presented based on new multimedia interactive terminal technology available also on smartphones. For potential erythema migrans skin lesion edge detection, we compared three different methods: GrowCut, maximal similarity based region merging and random walker segmentation method. The results obtained with GrowCut method are better than those obtained with random walker method. The GrowCut method, improved with our new finger draw (FD1) marker yields comparable results to those obtained with maximal similarity based region merging method. Several classification algorithms including naive Bayes, support vector machine, AdaBoost, random forest, and neural network were compared and used for classification of skin lesions into ellipse, the most common shape of erythema migrans and erythema migrans class.

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Autofluorescence Bronchoscopy Image Processing in the Selected Colour Spaces Authors

Cited by 3 publications

References 17 publications

Autofluorescence Bronchoscopy Video Analysis for Lesion Frame Detection

Autofluorescence Bronchoscopy Video Analysis for Lesion Frame Detection

ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video

Supervised Visual System for Recognition of Erythema Migrans, an Early Skin Manifestation of Lyme Borreliosis

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