Scikit-image Research Articles

3D airway geometry analysis of factors in airway navigation failure for lung nodules

BackgroundThis study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure.MethodsWe retrospectively reviewed patients who underwent R-EBUS between January 2017 and December 2018. Geometric quantification was analyzed using in-house software built with open-source python libraries including the Vascular Modeling Toolkit (http://www.vmtk.org), simple insight toolkit (https://sitk.org), and sci-kit image (https://scikit-image.org). We used a machine learning-based approach to explore the utility of these significant factors.ResultsOf the 491 patients who were eligible for analysis (mean age, 65 years +/- 11 [standard deviation]; 274 men), the target lesion was reached in 434 and was not reached in 57. Twenty-seven patients in the failure group were matched with 27 patients in the success group based on propensity scores. Bifurcation angle at the target branch, the least diameter of the last section, and the curvature of the last section are the most significant and stable factors for airway navigation failure. The support vector machine can predict airway navigation failure with an average area under the curve of 0.803.ConclusionsGeometric analysis in 3D space revealed that a large bifurcation angle and a narrow and tortuous structure of the closest bronchus from the lesion are associated with airway navigation failure during R-EBUS. The models developed using quantitative computer tomography scan imaging show the potential to predict airway navigation failure.

Comparing hybrid models for recognising objects in thermal images at nighttime

This research aims to revolutionize urban object recognition by developing cloud-based Python programs using intelligent algorithms. Unlike current models that focus on colour enhancement in nighttime thermal images, this work addresses the critical challenge of accurate object detection in urban landscapes. The proposed method incorporates a binary generative adversarial network (GAN) generator that can switch bidirectionally between daytime colour (DC) and nighttime infrared (NTIR) images. memory-based visual image memory (MVAM), system extracts important descriptive information from urban landscape images, reducing problems related to small sample sizes. This discussion presents a comprehensive improvement and evaluation of a deep learning image classification pipeline using Google Colab, demonstrating advanced image processing. Using TensorFlow, Keres and scikit image libraries combined with advanced algorithms such as DenseNet121 and MobileNetV2 presents a clear approach. We created a Bidirectional GAN + MVAM for object recognition in this work. Our method performed well, with an accuracy of 81.43%, precision of 51.16, recall of 50.11, and F-score of 46.37. The systematic presentation of the code presents a careful strategy to ensure optimal performance, stability, and efficiency of deep learning and image processing tasks.

Denoising in Magnetic Resonance Images using Improved Gaussian Smoothing Technique

Magnetic Resonance Images (MRI) are usually prone to noise like Rician and Gaussian noise. It is very difficult to perform image processing functions with the presence of noise. The objective of our work is to investigate the best method for denoising the MRI images. This study included 25 MRI subjects selected from the Open Access Series of Imaging Studies (OASIS) - 3 database. The 25 brain image subjects includes cases of both men and women aged 60 to 80. The input RGB image is first converted to gray scale image in which the contrast, sharpness, shadow and structure of the color of image are preserved. The proposed work uses an improved Gaussian smoothing technique for denoising Magnetic Resonance Images by constructing a modified mask for Gaussian smoothing. The performance of the proposed technique has been compared with various filters like median filter, Gaussian filter and Gabor filter. The performance evaluation was carried out by metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity (SSIM) index. The experimental results show that the Improved Gaussian Smoothing Technique (IGST) performs better than other methods. All experiments were conducted using Scikit Learn version 0.20 and Scikit Image version 0.14.1 under Python version 3.6.7.