RGB and HSV quantitative analysis of autofluorescence bronchoscopy used for characterization and identification of bronchopulmonary cancer

Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method

We evaluate the diagnostic performance of deep learning artificial intelligence (AI) for bladder cancer, which used white-light images (WLIs) and narrow-band images, and tumor grade prediction of AI based on tumor color using the red/green/blue (RGB) method. This retrospective study analyzed 10,991 cystoscopic images of suspicious bladder tumors using a mask region-based convolutional neural network with a ResNeXt-101-32 × 8d-FPN backbone. The diagnostic performance of AI was evaluated by calculating sensitivity, specificity, and diagnostic accuracy, and its ability to detect cancers was investigated using the dice score coefficient (DSC). Using the support vector machine model, we analyzed differences in tumor colors according to tumor grade using the RGB method. The sensitivity, specificity, diagnostic accuracy and DSC of AI were 95.0%, 93.7%, 94.1% and 74.7%. In WLIs, there were differences in red and blue values according to tumor grade (p < 0.001). According to the average RGB value, the performance was ≥ 98% for the diagnosis of benign vs. low-and high-grade tumors using WLIs and > 90% for the diagnosis of chronic non-specific inflammation vs. carcinoma in situ using WLIs. The diagnostic performance of the AI-assisted diagnosis was of high quality, and the AI could distinguish the tumor grade based on tumor color.

Classification of ballpoint pen inks based on selective extraction and subsequent digital color and cluster analyses

Extraction and color analysis coupled with machine learning allows clustering of pen inks and realizing preliminary classification when assessing document age.

Objective colour analysis from digital images as a nuclear forensic tool

A digital colour image may be composed of hundreds of thousands of pixels, every pixel exhibiting a single colour. Each colour can be described as a combination of red, green and blue (RGB) components, of discrete values between 0-255. The RGB data contained within the pixels of an image could, therefore, be used to quantitatively establish the colour of nuclear material powders from digital images, particularly for use in nuclear forensics applications, where there is a need for consistent, objective analysis. This paper sets out a standard method for the photography and analysis of digital images of uranium oxide powder, for the objective quantification of colour by mean RGB values. Eight heat treated (up to 550°C) powder samples of studtite ([(UO₂)(O₂)(H₂O)₂]·2H₂O) were photographed at room temperature and analysed by the RGB method. Hue, saturation and value of the coloured samples were obtained alongside mean RGB values, both of which were used to successfully determine the heating temperatures of unknown specimens of studtite.

Corneal-Smart Phone: A novel method to intelligently estimate postmortem interval

The changes of postmortem corneal opacity are often used to roughly estimate the postmortem interval (PMI) in forensic practice. The difficulty associated with this time estimate is the lack of objective means to rapidly quantify postmortem corneal changes in crime scenes. This study constructed a data analysis model of PMI estimation and implemented an intelligent analysis system for examining the sequential changes of postmortem corneal digital images, named Corneal-Smart Phone, which can be used to quickly estimate PMI. The smart phone was used in combination with an attachment device that provided a darkroom environment and a steady light source to capture postmortem corneal images. By segmenting the corneal pupil region images, six color features, Red (R), Green (G), Blue (B), Hue (H), Saturation (S), Brightness (V) and four texture features Contrast (CON), Correlation (COR), Angular Second Moment (ASM), and Homogeneity (HOM), were extracted and correlated with PMI model. The results indicated that CON had the highest correlation with PMI (R²  = 0.983). No intra/intersubject variation in CON values were observed (p > 0.05). With the increase in ambient temperature or the decrease in humidity, the CON values were increased. PMI prediction error was <3 h within 36 h postmortem and extended to about 6-8 h after 36 h postmortem. The correct classification rate of the blind test samples was 82%. Our study provides a method that combines postmortem corneal image acquisition and digital image analysis to enable users to quickly obtain PMI estimation.

A Tuberculous Bronchial Artery Aneurysm with Abnormal Findings on Autofluorescence Imaging Bronchoscopy

Pulmonary tuberculosis is a common disease that may result in hemoptysis. Fetal hemoptysis is known to be related to the rupture of a pulmonary aneurysm formed in the cavity wall. We herein report a case of non-cavity pulmonary tuberculosis that developed with massive hemoptysis following bronchial artery aneurysm. Bronchial artery embolization was performed, and autofluorescence imaging bronchoscopy was conducted one month after the anti-tuberculosis treatment. Bright-green color was observed in the ulcerative lesion with a white coat, corresponding to the bronchial artery aneurysm. This is the first report of the autofluorescence imaging observation of an ulcerative lesion caused by bronchial tuberculosis.

A bronchial fibroepithelial polyp with abnormal findings on auto-fluorescence imaging

Bronchial fibroepithelial polyps represent a rare type of tumour that displays endobronchial growth. The findings of these lesions on auto‐fluorescence imaging (AFI) bronchoscopy have not been reported, despite the usefulness of AFI in detecting early lung cancer. We report the case of a patient with a bronchial fibroepithelial polyp that displayed positivity (magenta colour) on AFI. The patient was a 65‐year‐old man, in whom an endobronchial polypoid lesion of 10 mm diameter had been detected in the right basal bronchus by chest computed tomography (CT). On bronchoscopic examination, we found a whitish, smooth polypoid lesion. The lesion appeared magenta on AFI. On CT, however, the lesion had been almost stable for 4 years and 4 months. Bronchial fibroepithelial polyps may show AFI positivity, even when the lesion displays benign behaviour. The diagnosis of the lesion should not be confused by AFI positivity, and unnecessary surgical intervention should be avoided.

RGB and HSV quantitative analysis of autofluorescence bronchoscopy used for characterization and identification of bronchopulmonary cancer

Autofluorescence bronchoscopy (AFB) shows good sensitivity in detecting dysplasia and bronchopulmonary cancer. However, the poor specificity of AFB would lead to excessive biopsy. The aim of the study is to establish a more effective quantitative method (optimal identification index and reference value) for characterizing the AFB images within the region of interest and discuss AFB's significance in the diagnosis of central‐type lung cancer. A total of 218 suspected lung cancer patients were enrolled in this study. A quantitative analysis based on color space (red, green, blue[RGB] and HSV system) was conducted and the result was compared with the final diagnosis obtained by the pathology of biopsy. Cases were divided into different groups according to the pathological diagnosis of normal bronchial mucosa, inflammation, low‐grade preinvasive (LGD), high‐grade preinvasive (HGD), and invasive cancer. Quantitative analyses in multi‐color spaces for the lesions showed by AFB images were conducted by software MATLAB. Finally, there is statistical significance among the different groups in some parameter in RGB and HSV system. So, both RGB and HSV quantitative analysis of autofluorescence bronchoscopy are useful to define benign and malignant diseases, which can objectively guide the bronchoscopist in selecting sites for biopsy with good pathologic correlation.