Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

Innovative Macula Capillaries Plexuses Visualization with OCTA B-Scan Graph Representation: Transforming OCTA B-Scan into OCTA Graph Representation.

Purpose: The aim of this study is to transform optical coherence tomography angiography (OCTA) scans into innovative OCTA graphs, serving as novel biomarkers representing the macular vasculature. Patients and Methods: The study included 90 healthy subjects and 39 subjects with various abnormalities (29 with diabetic retinopathy, 5 with age-related macular degeneration, and 5 with choroid neovascularization). OCTA 5µm macular coronal views (MCVs) were generated for each subject, followed by blood vessel segmentation and skeleton processing. Subsequently, the blood vessel density index, blood vessel skeleton index, and blood vessel tortuosity index were computed. The graphs of each metric were plotted against the axial axes of the OCTA B-scan, representing the integrity of vasculature at successive 5µm macular depths. Results: The results revealed two significant findings. First, the B-scans from OCTA can be transformed into OCTA graphs, yielding three specific OCTA graphs in this study. These graphs provide new biomarkers for assessing the integrity of deep vascular complex (DVC) and superficial vascular complex (SVC) within the macula. Second, a statistically significant difference was observed between normal (n=90) and abnormal (n=39) subjects, with a t-test p-value significantly lower than 0.001. The Mann-Whitney u-test also yielded significant difference but only between the 90 normal and 29 DR subjects. Conclusion: The novel OCTA graphs offer a unique representation of the macula's SVC and DVC, suggesting their potential in aiding physicians in the diagnosis of eye health within OCTA clinics. Further research is warranted to finalize the shape of these newly derived OCTA graphs and establish their clinical relevance and utility.

A New Prototype of Smart Wearable Monitoring System Solution for Alzheimer's Patients.

PURPOSE: The daily life management of patients with Alzheimer's disease (AD) constitutes a significant and rapidly expanding health-care responsibility. In this study, an innovative prototype of a wireless-sensing smart wearable medical device (SWMD) is proposed as a multi-functions solution for Alzheimer patients. The SWMD is aimed to assemble three main biomedical engineering advances: 1) use of a Wi-Fi microcontroller, 2) simultaneous monitoring of a set of vital biomarkers, and 3) cautions of fall down conditions, in addition to GPS location indicator. METHODS: The SWMD employs a Wi-Fi controller that is incorporated with electronic circuits to monitor three vital signals (temperature, heart rate, and oxygen saturation), fall down conditions in three directions (X, Y, and Z axis), and GPS location. The SWMD was connected to the Firebase Service (database hosted on the Internet Cloud). The proposed device was tested on 13 normal volunteers. The left side, right side, forward, and backward fall down conditions were assessed. The prototype's functions during daily activity such as rising hand, sitting down or standing up, and walking conditions were also assessed. RESULTS: The three assembled functions were all successfully incorporated to build the SWDM device as a suggested solution offering real-time alerts during daily activity to AD patients. The Bland-Altman statistical test showed no significant difference (p-value >0.05) between the SWMD biomarkers' acquisition and the reference methods. The gyro/accelerator sensor yielded 93% sensitivity in fall down detection and 95% specificity during daily activities. The GPS yielded correct positioning of the SWDM holder, while the internet cloud allowed saving and managing all vital biomarkers daily. CONCLUSION: The SWMD is a possible solution for daily life support for AD patients. It incorporates three functions in one single device, GPS location indicator, monitoring set of biomarkers, and fall down alert, which are all controlled via a Wi-Fi micro controller on-line connected to Internet Cloud. It successfully would allow the management of the daily records as well as the real-time alerts to remote persons.

Three-dimensional surface presentation of optic nerve head from SPECTRALIS OCT images: observing glaucoma patients.

PURPOSE: To propose an innovative three-dimensional surface presentation of the optic nerve head (ONH) from the SPECTRALIS optical coherence tomography (OCT) device. METHOD: A dataset of OCT ONH files from eight glaucoma follow-up patients was obtained. The set consisted of OCT ONH images for 20 right eyes (OD) and 17 left eyes (OS). Preprocessing steps followed with OCT reconstruction procedures were designed. The three-dimensional (3D) surface rendering was generated for all OCT ONH images. A set of eight International Organization for Standardization (ISO) roughness parameters were calculated to assess the disparities in the 3D ONH surface morphology during follow-up visit. RESULTS: The 3D ONH surface presents a new OCT display to ophthalmology; so, the physician can examine the surface morphology of the OCT ONH region. The 3D ONH surface's shape varied noticeably during follow-up visits in glaucoma patients. The percentage disparity of ONH surface roughness's can be as small as 3% or almost zero, but it can be as large as 56% or 100%. CONCLUSIONS: The approximation of OCT ONH 3D surface is feasible; it may possibly be beneficial to ophthalmology. It allows ophthalmologist to perceive the entire changes in the ONH surface morphology during the follow-up attendances; so, it can be used to observe patient health. The ISO roughness measurements are suggestive complementary factors to observe the alterations in the OCT ONH region.

Collaboration between interactive three-dimensional visualization and computer aided detection of pulmonary embolism on computed tomography pulmonary angiography views.

This paper explores the benefits of interactive three-dimensional (3D) visualization of stimuli using a computer aided detection (CAD) system of pulmonary embolisms (PEs), on computed tomography pulmonary angiography (CTPA) views. We designed a new CAD method that prompts the PE sites on CTPA views; we then utilized two interactive approaches of 3D visualization to assess CAD performance. This collaboration allows different methodologies to be used to assess PE-CAD performance, by comparison with the common method in which radiologists are prompted with CAD stimuli directly on the CTPA views. Both 3D approaches are based on the voxel size of the CTPA examination, and consider the acquisition settings. A set of ten retrospective CTPA cases were collected, with different acquisition parameters, in terms of voxel size and spatial resolution. 3D visualization CAD performance was examined by an experienced radiologist. Both 3D visualization methods proved to have a constructive impact on improving CAD performance. The rate of true positive (TP) responses increased by 27%; while the rate of false positive (FP) responses dropped by 31%. We concluded that evaluation of PE-CAD performance utilizing interactive 3D visualization could increase or ascertain the correct rate of TP stimuli, as well as noticeably reduce FP responses.

Optic nerve head slope-based quantitative parameters for identifying open-angle glaucoma on SPECTRALIS OCT images.

PURPOSE: To investigate monitoring slope-based features of the optic nerve head (ONH) cup as open-angle glaucoma (OAG) occurs. METHOD: A dataset of 46 retrospective OCT cases was acquired from the SPECTRALIS Heidelberg Engineering OCT device. A set of five parameters, which are based on the ONH cup-incline, are measured on the OAG and normal subjects in the dataset. Then, three new ONH cup-shape indices were deduced. The ONH cup-incline parameters and ONH cup-shape indices are analyzed to estimate their clinical value. RESULTS: The statistical difference between measurements on normal and glaucoma eyes was remarkably significant for all of the analyzed parameters and indices (p value < 0.001). CONCLUSIONS: The geometric shape of the ONH cup can be transferred to numerical parameters and indices. The proposed ONH cup-incline parameters and ONH cup-shape indices have shown suggestive clinical value to identify the development of OAG. As OAG appears, the top ONH cup-incline parameters decrease while the bottom ONH cup-incline parameters increase. The ONH cup-shape indices suggest capability to discriminate OAG from normal eyes.

Assessment of bilateral filter on 1/2-dose chest-pelvis CT views.

A bilateral filter (BF) is a non-linear filter that has been proved to de-noise images without overrunning edges. Multi-slice computerized tomography (CT) may employ a BF to participate in dose reduction. This paper quantifies the role of the BF in achieving this objective on 1/2-dose CT. Two sets of CT images are acquired for the chest-pelvis at two different radiation doses. The BF was applied on the 1/2-dose CT images by use of various window sizes. Each time, a set of values of the BF range was fixed while the BF domain was modified. The goal was to observe the behavior of the BF on 1/2-dose CT images in comparison with full-dose CT images. The comparison was carried out by use of four co-occurrence matrix descriptors. Additionally, the peak signal-to-noise ratio (PSNR) and the mean square error (MSE) were reported. The study was applied to the sagittal, coronal, and axial CT views. The results showed that the impact of applying a BF varies among different CT views. The BF can retrieve only part of the signal being lost due to reduction of the radiation dose by one half. Yet, the BF improves the appearance of the 1/2-dose chest-pelvis CT examination. Thus, the BF can contribute to a 50% dose reduction. A procedure for employing the BF on CT machines is proposed. The results also showed that texture descriptors are similar to the PSNR and MSE in providing quantities for assessing medical image quality.